The 2017 Release of Cloudy

G. J. Ferland; M. Chatzikos; F. Guzm\'an; M. L. Lykins; P. A. M. van; Hoof; R. J. R. Williams; N. P. Abel; N. R. Badnell; F. P. Keenan; R. L.; Porter; P. C. Stancil

arXiv:1705.10877·astro-ph.GA·October 9, 2023

The 2017 Release of Cloudy

G. J. Ferland, M. Chatzikos, F. Guzm\'an, M. L. Lykins, P. A. M. van, Hoof, R. J. R. Williams, N. P. Abel, N. R. Badnell, F. P. Keenan, R. L., Porter, P. C. Stancil

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TL;DR

The 2017 release of Cloudy introduces external atomic data files, significantly expanding emission line predictions, optimizing performance, and enhancing physical modeling capabilities for astrophysical plasma simulations.

Contribution

This release uniquely incorporates external atomic datasets, improves computational efficiency, and advances modeling of ionization processes and non-equilibrium cloud evolution.

Findings

01

Predicts over an order of magnitude more emission lines.

02

Faster performance despite larger datasets.

03

Establishes density thresholds for ionization approximations.

Abstract

We describe the 2017 release of the spectral synthesis code Cloudy. A major development since the previous release has been exporting the atomic data into external data files. This greatly simplifies updates and maintenance of the data. Many large datasets have been incorporated with the result that we can now predict well over an order of magnitude more emission lines when all databases are fully used. The use of such large datasets is not realistic for most calculations due to the time and memory needs, and we describe the limited subset of data we use by default. Despite the fact that we now predict significantly more lines than the previous Cloudy release, this version is faster because of optimization of memory access patterns and other tuning. The size and use of the databases can easily be adjusted in the command-line interface. We give examples of the accuracy limits using small…

Tables17

Table 1. Table 4: Collisional equilibrium ionization fractions

Log Te	He	He+1	He+2
$[K]$
3.00	0.00	–	–
3.10	0.00	–	–
⋮	⋮	⋮	⋮
3.90	0.00	–	–
4.00	0.00	–	–
4.10	-0.00	-6.45	–
4.20	-0.00	-3.93	–
4.30	-0.00	-2.16	–
4.40	-0.07	-0.81	-8.73
4.50	-0.55	-0.15	-5.71
4.60	-1.35	-0.02	-3.70
4.70	-2.07	-0.01	-2.16
4.80	-2.67	-0.05	-0.97
4.90	-3.33	-0.34	-0.26
5.00	-4.15	-0.95	-0.05
⋮	⋮	⋮	⋮
7.00	–	-5.89	-0.00
7.10	–	-6.00	-0.00
7.20	–	-6.11	-0.00
⋮	⋮	⋮	⋮
8.70	–	-7.78	0.00
8.80	–	-7.90	0.00
8.90	–	-8.02	0.00
9.00	–	-8.14	0.00

Table 2. Table 8: Species data bases.

Species	Database	Species	Database	Species	Database	Species	Database
H	H-like	O⁷⁺	H-like	Mg⁴⁺	Chianti	P	Stout
He	He-like	F	…	Mg⁵⁺	Chianti	P⁺	Stout
He⁺	H-like	F⁺	Stout	Mg⁶⁺	Chianti	P²⁺	Stout
Li	Stout	F²⁺	Stout	Mg⁷⁺	Chianti	P³⁺	Stout
Li⁺	He-like	F³⁺	Stout	Mg⁸⁺	Chianti	P⁴⁺	Chianti
Li²⁺	H-like	F⁴⁺	Stout	Mg⁹⁺	Chianti	P⁵⁺	Stout
Be	Stout	F⁵⁺	Stout	Mg¹⁰⁺	He-like	P⁶⁺	Chianti
Be⁺	Stout	F⁶⁺	Stout	Mg¹¹⁺	H-like	P⁷⁺	Chianti
Be²⁺	He-like	F⁷⁺	He-like	Al	Stout	P⁸⁺	Chianti
Be³⁺	H-like	F⁸⁺	H-like	Al⁺	Chianti	P⁹⁺	Chianti
B	Stout	Ne	Stout	Al²⁺	Stout	P¹⁰⁺	Chianti
B⁺	Stout	Ne⁺	Stout	Al³⁺	Stout	P¹¹⁺	Chianti
B²⁺	Stout	Ne²⁺	Stout	Al⁴⁺	Chianti	P¹²⁺	Chianti
B³⁺	He-like	Ne³⁺	Chianti	Al⁵⁺	Stout	P¹³⁺	He-like
B⁴⁺	H-like	Ne⁴⁺	Chianti	Al⁶⁺	Chianti	P¹⁴⁺	H-like
C	Stout	Ne⁵⁺	Chianti	Al⁷⁺	Chianti	S	Stout
C⁺	Stout	Ne⁶⁺	Chianti	Al⁸⁺	Chianti	S⁺	Stout
C²⁺	Stout	Ne⁷⁺	Chianti	Al⁹⁺	Chianti	S²⁺	Stout
C³⁺	Chianti	Ne⁸⁺	He-like	Al¹⁰⁺	Chianti	S³⁺	Chianti
C⁴⁺	He-like	Ne⁹⁺	H-like	Al¹¹⁺	He-like	S⁴⁺	Chianti
C⁵⁺	H-like	Na	Stout	Al¹²⁺	H-like	S⁵⁺	Chianti
N	Stout	Na⁺	Stout	Si	Stout	S⁶⁺	Chianti
N⁺	Chianti	Na²⁺	Chianti	Si⁺	Stout	S⁷⁺	Chianti
N²⁺	Chianti	Na³⁺	Chianti	Si²⁺	Stout	S⁸⁺	Chianti
N³⁺	Chianti	Na⁴⁺	Chianti	Si³⁺	Stout	S⁹⁺	Chianti
N⁴⁺	Stout	Na⁵⁺	Chianti	Si⁴⁺	Chianti	S¹⁰⁺	Chianti
N⁵⁺	He-like	Na⁶⁺	Chianti	Si⁵⁺	Chianti	S¹¹⁺	Chianti
N⁶⁺	H-like	Na⁷⁺	Chianti	Si⁶⁺	Stout	S¹²⁺	Chianti
O	Stout	Na⁸⁺	Chianti	Si⁷⁺	Chianti	S¹³⁺	Chianti
O⁺	Stout	Na⁹⁺	He-like	Si⁸⁺	Stout	S¹⁴⁺	He-like
O²⁺	Stout	Na¹⁰⁺	H-like	Si⁹⁺	Chianti	S¹⁵⁺	H-like
O³⁺	Chianti	Mg	Stout	Si¹⁰⁺	Chianti	Cl	Stout
O⁴⁺	Stout	Mg⁺	Chianti	Si¹¹⁺	Chianti	Cl⁺	Chianti
O⁵⁺	Chianti	Mg²⁺	Stout	Si¹²⁺	He-like	Cl²⁺	Chianti
O⁶⁺	He-like	Mg³⁺	Chianti	Si¹³⁺	H-like	Cl³⁺	Chianti

Table 3. Table 8: (Cont.)

Species	Database	Species	Database	Species	Database	Species	Database
Cl⁴⁺	Stout	K⁴⁺	Chianti	Sc	Stout	Ti¹⁴⁺	Chianti
Cl⁵⁺	Stout	K⁵⁺	Chianti	Sc⁺	Stout	Ti¹⁵⁺	Chianti
Cl⁶⁺	Stout	K⁶⁺	Stout	Sc²⁺	Stout	Ti¹⁶⁺	Chianti
Cl⁷⁺	Stout	K⁷⁺	Stout	Sc³⁺	Stout	Ti¹⁷⁺	Chianti
Cl⁸⁺	Stout	K⁸⁺	Chianti	Sc⁴⁺	Stout	Ti¹⁸⁺	Chianti
Cl⁹⁺	Chianti	K⁹⁺	Stout	Sc⁵⁺	Stout	Ti¹⁹⁺	Chianti
Cl¹⁰⁺	Chianti	K¹⁰⁺	Chianti	Sc⁶⁺	Stout	Ti²⁰⁺	He-like
Cl¹¹⁺	Chianti	K¹¹⁺	Chianti	Sc⁷⁺	Stout	Ti²¹⁺	H-like
Cl¹²⁺	…	K¹²⁺	Chianti	Sc⁸⁺	Stout	V	…
Cl¹³⁺	Chianti	K¹³⁺	Chianti	Sc⁹⁺	Stout	V⁺	…
Cl¹⁴⁺	…	K¹⁴⁺	Chianti	Sc¹⁰⁺	Stout	V²⁺	…
Cl¹⁵⁺	He-like	K¹⁵⁺	Chianti	Sc¹¹⁺	Stout	V³⁺	Stout
Cl¹⁶⁺	H-like	K¹⁶⁺	Chianti	Sc¹²⁺	Stout	V⁴⁺	…
Ar	Stout	K¹⁷⁺	He-like	Sc¹³⁺	Stout	V⁵⁺	Stout
Ar⁺	Stout	K¹⁸⁺	H-like	Sc¹⁴⁺	Stout	V⁶⁺	Stout
Ar²⁺	Stout	Ca	Stout	Sc¹⁵⁺	Stout	V⁷⁺	Stout
Ar³⁺	Stout	Ca⁺	Chianti	Sc¹⁶⁺	Stout	V⁸⁺	Stout
Ar⁴⁺	Stout	Ca²⁺	Stout	Sc¹⁷⁺	Stout	V⁹⁺	Stout
Ar⁵⁺	Stout	Ca³⁺	Stout	Sc¹⁸⁺	…	V¹⁰⁺	Stout
Ar⁶⁺	Chianti	Ca⁴⁺	Chianti	Sc¹⁹⁺	He-like	V¹¹⁺	Stout
Ar⁷⁺	Chianti	Ca⁵⁺	Stout	Sc²⁰⁺	H-like	V¹²⁺	Stout
Ar⁸⁺	Chianti	Ca⁶⁺	Chianti	Ti	…	V¹³⁺	…
Ar⁹⁺	Chianti	Ca⁷⁺	Chianti	Ti⁺	…	V¹⁴⁺	Stout
Ar¹⁰⁺	Chianti	Ca⁸⁺	Chianti	Ti²⁺	Stout	V¹⁵⁺	Stout
Ar¹¹⁺	Chianti	Ca⁹⁺	Chianti	Ti³⁺	Stout	V¹⁶⁺	Stout
Ar¹²⁺	Chianti	Ca¹⁰⁺	Chianti	Ti⁴⁺	Stout	V¹⁷⁺	Stout
Ar¹³⁺	Chianti	Ca¹¹⁺	Chianti	Ti⁵⁺	Stout	V¹⁸⁺	Stout
Ar¹⁴⁺	Chianti	Ca¹²⁺	Chianti	Ti⁶⁺	Stout	V¹⁹⁺	Stout
Ar¹⁵⁺	Chianti	Ca¹³⁺	Chianti	Ti⁷⁺	Stout	V²⁰⁺	Stout
Ar¹⁶⁺	He-like	Ca¹⁴⁺	Chianti	Ti⁸⁺	Stout	V²¹⁺	He-like
Ar¹⁷⁺	H-like	Ca¹⁵⁺	Chianti	Ti⁹⁺	Stout	V²²⁺	H-like
K	Stout	Ca¹⁶⁺	Chianti	Ti¹⁰⁺	Chianti	Cr	…
K⁺	Stout	Ca¹⁷⁺	Chianti	Ti¹¹⁺	Chianti	Cr⁺	Stout
K²⁺	Stout	Ca¹⁸⁺	He-like	Ti¹²⁺	Stout	Cr²⁺	…
K³⁺	Stout	Ca¹⁹⁺	H-like	Ti¹³⁺	Chianti	Cr³⁺	Stout

Table 4. Table 8: (Cont.)

Species	Database	Species	Database	Species	Database	Species	Database
Cr⁴⁺	Stout	Mn¹⁵⁺	Stout	Fe²⁵⁺	H-like	Ni⁶⁺	Stout
Cr⁵⁺	…	Mn¹⁶⁺	Chianti	Co	…	Ni⁷⁺	…
Cr⁶⁺	Chianti	Mn¹⁷⁺	Chianti	Co⁺	…	Ni⁸⁺	Stout
Cr⁷⁺	Chianti	Mn¹⁸⁺	Chianti	Co⁺	Stout	Ni⁹⁺	…
Cr⁸⁺	Chianti	Mn¹⁹⁺	Chianti	Co²⁺	Stout	Ni¹⁰⁺	Stout
Cr⁹⁺	Stout	Mn²⁰⁺	Chianti	Co³⁺	…	Ni¹¹⁺	Chianti
Cr¹⁰⁺	Stout	Mn²¹⁺	Chianti	Co⁴⁺	…	Ni¹²⁺	Chianti
Cr¹¹⁺	Stout	Mn²²⁺	Chianti	Co⁵⁺	…	Ni¹³⁺	Chianti
Cr¹²⁺	Chianti	Mn²³⁺	He-like	Co⁶⁺	…	Ni¹⁴⁺	Stout
Cr¹³⁺	Chianti	Mn²⁴⁺	H-like	Co⁷⁺	Stout	Ni¹⁵⁺	Chianti
Cr¹⁴⁺	Stout	Fe	Stout	Co⁸⁺	…	Ni¹⁶⁺	Chianti
Cr¹⁵⁺	Chianti	Fe⁺	Stout	Co⁹⁺	Stout	Ni¹⁷⁺	Chianti
Cr¹⁶⁺	Chianti	Fe²⁺	Stout	Co¹⁰⁺	Stout	Ni¹⁸⁺	Chianti
Cr¹⁷⁺	Chianti	Fe³⁺	Chianti	Co¹¹⁺	Stout	Ni¹⁹⁺	Chianti
Cr¹⁸⁺	Chianti	Fe⁴⁺	Chianti	Co¹²⁺	Stout	Ni²⁰⁺	Chianti
Cr¹⁹⁺	Chianti	Fe⁵⁺	Chianti	Co¹³⁺	Stout	Ni²¹⁺	Chianti
Cr²⁰⁺	Chianti	Fe⁶⁺	Stout	Co¹⁴⁺	Stout	Ni²²⁺	Chianti
Cr²¹⁺	Chianti	Fe⁷⁺	Chianti	Co¹⁵⁺	Stout	Ni²³⁺	Chianti
Cr²²⁺	He-like	Fe⁸⁺	Stout	Co¹⁶⁺	Chianti	Ni²⁴⁺	Chianti
Cr²³⁺	H-like	Fe⁹⁺	Chianti	Co¹⁷⁺	Stout	Ni²⁵⁺	Chianti
Mn	Stout	Fe¹⁰⁺	Chianti	Co¹⁸⁺	Chianti	Ni²⁶⁺	He-like
Mn⁺	…	Fe¹¹⁺	Chianti	Co¹⁹⁺	Chianti	Ni²⁷⁺	H-like
Mn²⁺	…	Fe¹²⁺	Chianti	Co²⁰⁺	Chianti	Cu	Stout
Mn³⁺	…	Fe¹³⁺	Chianti	Co²¹⁺	Chianti	Cu⁺	…
Mn⁴⁺	Stout	Fe¹⁴⁺	Chianti	Co²²⁺	Chianti	Cu²⁺	…
Mn⁵⁺	Stout	Fe¹⁵⁺	Chianti	Co²³⁺	Chianti	Cu³⁺	…
Mn⁶⁺	…	Fe¹⁶⁺	Chianti	Co²⁴⁺	Chianti	Cu⁴⁺	…
Mn⁷⁺	Chianti	Fe¹⁷⁺	Chianti	Co²⁵⁺	He-like	Cu⁵⁺	…
Mn⁸⁺	Chianti	Fe¹⁸⁺	Chianti	Co²⁶⁺	H-like	Cu⁶⁺	…
Mn⁹⁺	Chianti	Fe¹⁹⁺	Chianti	Ni	Stout	Cu⁷⁺	…
Mn¹⁰⁺	Stout	Fe²⁰⁺	Chianti	Ni⁺	Chianti	Cu⁸⁺	…
Mn¹¹⁺	Stout	Fe²¹⁺	Chianti	Ni²⁺	Stout	Cu⁹⁺	…
Mn¹²⁺	Stout	Fe²²⁺	Chianti	Ni³⁺	Stout	Cu¹⁰⁺	…
Mn¹³⁺	Stout	Fe²³⁺	Chianti	Ni⁴⁺	Stout	Cu¹¹⁺	…
Mn¹⁴⁺	Chianti	Fe²⁴⁺	He-like	Ni⁵⁺	…	Cu¹²⁺	Stout

Table 5. Table 8: (Cont.)

Species	Database	Species	Database	Species	Database	Species	Database
Cu¹³⁺	Stout	Zn¹⁹⁺	Chianti	CN	LAMDA	HTiS	…
Cu¹⁴⁺	Stout	Zn²⁰⁺	…	CN⁺	…	HTiS⁺	…
Cu¹⁵⁺	Stout	Zn²¹⁺	…	CO	LAMDA	HeH⁺	…
Cu¹⁶⁺	Stout	Zn²²⁺	Chianti	CS	LAMDA	Li^-	…
Cu¹⁷⁺	Stout	Zn²³⁺	Chianti	CS⁺	…	LiH	…
Cu¹⁸⁺	…	Zn²⁴⁺	Chianti	ClO	…	LiH⁺	…
Cu¹⁹⁺	…	Zn²⁵⁺	…	ClO⁺	…	N₂	…
Cu²⁰⁺	Stout	Zn²⁶⁺	Chianti	H^-	…	N₂⁺	…
Cu²¹⁺	Stout	Zn²⁷⁺	Chianti	H₂	…	N₂H⁺	LAMDA
Cu²²⁺	Stout	Zn²⁸⁺	He-like	H₂^⋆	…	N₂O	…
Cu²³⁺	Stout	Zn²⁹⁺	H-like	H₂⁺	…	NH	…
Cu²⁴⁺	Stout	ArH⁺	…	H₂CCl⁺	…	NH⁺	…
Cu²⁵⁺	…	C₂	…	H₂CO	LAMDA	NH₂	…
Cu²⁶⁺	…	C₂⁺	…	H₂Cl⁺	…	NH₂⁺	…
Cu²⁷⁺	He-like	C₂H	LAMDA	H₂O	LAMDA	NH₃	LAMDA
Cu²⁸⁺	H-like	C₂H⁺	…	H₂O⁺	…	NH₃⁺	…
Zn	…	C₂H₂	…	H₃⁺	…	NH₄⁺	…
Zn⁺	Stout	C₂H₂⁺	…	H₃O⁺	LAMDA	NO	LAMDA
Zn²⁺	…	C₂H₃⁺	…	HC₃N	LAMDA	NO⁺	…
Zn³⁺	Stout	C₃	…	HCN	LAMDA	NO₂	…
Zn⁴⁺	…	C₃⁺	…	HCN⁺	…	NO₂⁺	…
Zn⁵⁺	…	C₃H	…	HCNH⁺	…	NS	…
Zn⁶⁺	…	C₃H⁺	…	HCO⁺	LAMDA	NS⁺	…
Zn⁷⁺	…	CCl	…	HCS⁺	LAMDA	NeH⁺	…
Zn⁸⁺	…	CCl⁺	…	HCTi	…	O₂	LAMDA
Zn⁹⁺	…	CH	…	HCTi⁺	…	O₂⁺	…
Zn¹⁰⁺	…	CH⁺	…	HCl	LAMDA	OCN	…
Zn¹¹⁺	…	CH₂	…	HCl⁺	…	OCN⁺	…
Zn¹²⁺	…	CH₂⁺	…	HNC	LAMDA	OCS	LAMDA
Zn¹³⁺	…	CH₃	…	HNO	…	OCS⁺	…
Zn¹⁴⁺	…	CH₃⁺	…	HNO⁺	…	OH	LAMDA
Zn¹⁵⁺	…	CH₃OH	LAMDA	HNTi	…	OH⁺	LAMDA
Zn¹⁶⁺	…	CH₄	…	HNTi⁺	…	S₂	…
Zn¹⁷⁺	…	CH₄⁺	…	HS	…	S₂⁺	…
Zn¹⁸⁺	Stout	CH₅⁺	…	HS⁺	…	SO	LAMDA

Table 6. Table 8: (Cont.)

Species	Database	Species	Database	Species	Database	Species	Database
SO⁺	…	SiO⁺	…	TiH⁺	…	TiO⁺	…
SO₂	LAMDA	SiOH⁺	…	TiH₂	…	TiO₂	…
SiC₂	LAMDA	TiC	…	TiH₂⁺	…	TiOH⁺	…
SiH	…	TiC⁺	…	TiN	…	TiS	…
SiH₂⁺	…	TiC₂	…	TiN⁺	…	TiS⁺	…
SiN	…	TiC₂⁺	…	TiNC	…	¹³CO	LAMDA
SiN⁺	…	TiF⁺	…	TiNC⁺	…
SiO	LAMDA	TiH	…	TiO	…

Table 7. Table 9: References for Stout database. Species marked with ‡ ‡ \ddagger are listed in Stout.ini, the default masterlist.

Species	Energy	Transition	Collision
Li $‡$	NIST 2013-11-08	NIST 2013-10-18	baseline
Be $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Be⁺ $‡$	NIST 2013-11-08	NIST 2013-10-18	baseline
B $‡$	NIST 2013-10-18	NIST 2013-10-18	baseline
B⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
B²⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
C $‡$	NIST 2013-10-01	NIST 2013-10-01	Johnson et al. (1987)
			Mendoza (1983)
			Abrahamsson et al. (2007)
			Roueff & Le Bourlot (1990)
			Schroder et al. (1991)
			Staemmler & Flower (1991)
C⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	Goldsmith et al. (2012)
			Wiesenfeld & Goldsmith (2014)
			Tayal (2008)
C²⁺ $‡$	NIST 2013-11-08	NIST 2013-10-20	Berrington et al. (1985)
	Fernández-Menchero et al. (2014a)	Fernández-Menchero et al. (2014a)	Fernández-Menchero et al. (2014a)
C³⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
N $‡$	NIST 2013-12-05	Froese Fischer & Tachiev (2004)	Tayal (2006)
		NIST 2013-12-05
N⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
N²⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
N³⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
N⁴⁺ $‡$	NIST 2013-12-05	NIST 2013-12-05	baseline
O $‡$	NIST 2014-01-09	NIST 2014-01-09	Bell et al. (1998)
			Barklem (2007)
			Abrahamsson et al. (2007)
			Krems et al. (2006)
			Monteiro & Flower (1987)
			Jaquet et al. (1992)
			Pequignot (1990)
			Wang & McConkey (1992)

Table 8. Table 9: (Cont.)

Species	Energy	Transition	Collision
O⁺ $‡$	NIST 2014-01-26	Froese Fischer & Tachiev (2004)	Kisielius et al. (2009)
		NIST 2014-01-26
O²⁺ $‡$	NIST 2014-09-16	NIST	Aggarwal (1983)
		Galavis et al. (1997)	Aggarwal (1985)
		Storey & Zeippen (2000)	Bhatia & Kastner (1993)
		Tachiev & Froese Fischer (2001)	Lennon & Burke (1994)
			Storey et al. (2014)
O⁴⁺	NIST 2014-09-16	NIST 2014-09-16	Fernández-Menchero et al. (2014a)
	Fernández-Menchero et al. (2014a)	Fernández-Menchero et al. (2014a)
O⁵⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
F⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	Butler & Zeippen (1994)
F²⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
F³⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	Lennon & Burke (1994)
F⁴⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
F⁵⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	Fernández-Menchero et al. (2014a)
	Fernández-Menchero et al. (2014a)	Fernández-Menchero et al. (2014a)
F⁶⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Ne $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Ne⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	Griffin et al. (2001)
			Hollenbach & McKee (1989)
Ne²⁺ $‡$	NIST	McLaughlin et al. (2011)	McLaughlin et al. (2011)
	McLaughlin et al. (2011)
Ne³⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ne⁴⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ne⁶⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ne⁷⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Na $‡$	NIST 2013-10-01	NIST 2013-10-01	Verner, private communication
Na⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Na²⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Na³⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Na⁴⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Na⁵⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Na⁶⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Na⁷⁺	NIST 2014-09-16	NIST 2014-09-16	baseline

Table 9. Table 9: (Cont.)

Species	Energy	Transition	Collision
Na⁸⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Mg $‡$	NIST 2013-10-01	NIST 2013-10-01	Barklem et al. (2012)
			Osorio et al. (2015)
Mg⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Mg²⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	Liang & Badnell (2010)
			NIST 2013-09-07
Mg³⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Mg⁴⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Mg⁵⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Mg⁶⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Mg⁷⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Mg⁸⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Mg⁹⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Al $‡$	NIST 2013-10-01	NIST 2013-10-01	baseline
Al⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Al²⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	Dufton & Kingston (1987)
Al³⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Al⁴⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Al⁵⁺ $‡$	NIST 2014-05-24	NIST 2014-05-24	Butler & Zeippen (1994)
Al⁶⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Al⁷⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Al⁸⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Al⁹⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Al¹⁰⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Si $‡$	NIST 2013-10-01	NIST 2013-10-01	Hollenbach & McKee (1989)
Si⁺ $‡$	NIST 2014-08-01	Laha et al. (2016)	Aggarwal & Keenan (2014)
	Laha et al. (2016)		Barinovs et al. (2005)
			Laha et al. (2016)
Si²⁺ $‡$	Fernández-Menchero et al. (2014b)	Kwong et al. (1983)	Fernández-Menchero et al. (2014b)
	NIST	Fernández-Menchero et al. (2014b)
Si³⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	Liang et al. (2009)
Si⁴⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Si⁵⁺	NIST 2014-09-16	NIST 2014-09-16	baseline

Table 10. Table 9: (Cont.)

Species	Energy	Transition	Collision
Si⁶⁺ $‡$	NIST 2014-05-24	NIST 2014-05-24	Butler & Zeippen (1994)
Si⁷⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Si⁸⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	Lennon & Burke (1994)
Si⁹⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Si¹⁰⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Si¹¹⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
P $‡$	NIST 2013-10-18	NIST 2013-10-18	baseline
P⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	Krueger & Czyzak (1970)
P²⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	Krueger & Czyzak (1970)
P³⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
P⁴⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
P⁵⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
P⁶⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
P⁷⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
P⁸⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
P¹¹⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
P¹²⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
S $‡$	NIST 2013-10-01	NIST 2013-10-01	Hollenbach & McKee (1989)
S⁺ $‡$	NIST	Kisielius et al. (2014)	Tayal & Zatsarinny (2010)
S²⁺ $‡$		Froese Fischer et al. (2006)	Hudson et al. (2012b)
		Tayal (1997)
		Podobedova et al. (2009)
		Heise et al. (1995)
S³⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
S⁴⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
S⁵⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
S⁶⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
S⁷⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
S⁸⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
S⁹⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
S¹⁰⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
S¹¹⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
S¹²⁺	NIST 2014-09-16	NIST 2014-09-16	baseline

Table 11. Table 9: (Cont.)

Species	Energy	Transition	Collision
S¹³⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Cl $‡$	NIST 2013-10-01	NIST 2013-10-01	Hollenbach & McKee (1989)
Cl⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Cl²⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Cl³⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Cl⁴⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Cl⁵⁺ $‡$	NIST	Fernández-Menchero et al. (2014b)	Fernández-Menchero et al. (2014b)
	Fernández-Menchero et al. (2014b)
Cl⁶⁺ $‡$	Liang et al. (2009)	Liang et al. (2009)	Liang et al. (2009)
Cl⁷⁺ $‡$	Liang & Badnell (2010)	Liang & Badnell (2010)	Liang & Badnell (2010)
Cl⁸⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	Berrington et al. (1998)
Cl⁹⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ar $‡$	NIST 2013-11-08	NIST 2013-10-01	baseline
Ar⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	Pelan & Berrington (1995)
Ar²⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	Galavis et al. (1995)
Ar³⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	Ramsbottom et al. (1997)
Ar⁴⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	Galavis et al. (1995)
Ar⁵⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	Saraph & Storey (1996)
Ar⁶⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ar⁷⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ar⁸⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ar⁹⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ar¹⁰⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ar¹²⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ar¹³⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
K $‡$	NIST 2013-10-01	NIST 2013-10-01	baseline
K⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
K²⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	Pelan & Berrington (1995)
K³⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	Galavis et al. (1995)
K⁴⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
K⁵⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
K⁶⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	Saraph & Storey (1996)

Table 12. Table 9: (Cont.)

Species	Energy	Transition	Collision
K⁷⁺ $‡$	NIST	Fernández-Menchero et al. (2014b)	Fernández-Menchero et al. (2014b)
	Fernández-Menchero et al. (2014b)
K⁸⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
K⁹⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
K¹⁰⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
K¹¹⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
K¹²⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
K¹³⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
K¹⁴⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
K¹⁵⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
K¹⁶⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ca $‡$	NIST 2013-10-01	NIST 2013-10-01	baseline
Ca⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ca²⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Ca³⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	Pelan & Berrington (1995)
Ca⁴⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ca⁵⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Ca⁶⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ca⁷⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ca⁹⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ca¹⁰⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ca¹¹⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ca¹²⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ca¹⁴⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Sc $‡$	NIST 2013-10-18	NIST 2013-10-18	baseline
Sc⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	Wasson et al. (2011)
Sc²⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	baseline
Sc³⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Sc⁴⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	Pelan & Berrington (1995)
Sc⁵⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	baseline
Sc⁶⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Sc⁷⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Sc⁸⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline

Table 13. Table 9: (Cont.)

Species	Energy	Transition	Collision
Sc⁹⁺ $‡$	NIST	Fernández-Menchero et al. (2014b)	Fernández-Menchero et al. (2014b)
	Fernández-Menchero et al. (2014b)
Sc¹⁰⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Sc¹¹⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Sc¹²⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	Saraph & Tully (1994)
Sc¹³⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Sc¹⁴⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Sc¹⁵⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Sc¹⁶⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Sc¹⁷⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Ti²⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	baseline
Ti³⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Ti⁴⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Ti⁵⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	Pelan & Berrington (1995)
Ti⁶⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Ti⁷⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Ti⁸⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Ti⁹⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Ti¹⁰⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ti¹¹⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ti¹²⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Ti¹³⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ti¹⁴⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ti¹⁵⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ti¹⁶⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ti¹⁷⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ti¹⁸⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ti¹⁹⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
V³⁺ $‡$	NIST	NIST	baseline
V⁵⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
V⁶⁺ $‡$	NIST	NIST	Pelan & Berrington (1995)
V⁷⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
V⁸⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
V⁹⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline

Table 14. Table 9: (Cont.)

Species	Energy	Transition	Collision
V¹⁰⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
V¹¹⁺ $‡$	NIST	Fernández-Menchero et al. (2014b)	Fernández-Menchero et al. (2014b)
V¹²⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
V¹⁴⁺ $‡$	NIST	NIST	Berrington et al. (1998)
	NIST	NIST
V¹⁵⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
V¹⁶⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
V¹⁷⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
V¹⁸⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
V¹⁹⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
V²⁰⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Cr⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	Grieve & Ramsbottom (2012)
Cr³⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	baseline
Cr⁴⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	baseline
Cr⁶⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Cr⁸⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Cr⁹⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Cr¹⁰⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Cr¹¹⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Cr¹²⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Cr¹³⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Cr¹⁴⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Cr¹⁵⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Cr¹⁶⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Cr¹⁷⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Cr¹⁸⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Cr¹⁹⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Cr²⁰⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Cr²¹⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Mn $‡$	NIST 2013-11-08	NIST 2013-10-18	baseline
Mn⁴⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Mn⁵⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Mn⁷⁺	NIST 2014-09-16	NIST 2014-09-16	baseline

Table 15. Table 9: (Cont.)

Species	Energy	Transition	Collision
Mn⁸⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Mn⁹⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Mn¹⁰⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Mn¹¹⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Mn¹²⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Mn¹³⁺ $‡$	NIST	Fernández-Menchero et al. (2014b)	Fernández-Menchero et al. (2014b)
	Fernández-Menchero et al. (2014b)
Mn¹⁴⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Mn¹⁵⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Mn¹⁶⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Mn¹⁷⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Mn¹⁸⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Mn¹⁹⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Mn²⁰⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Mn²¹⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Mn²²⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Fe $‡$	NIST 2013-10-18	NIST 2013-10-18	Hollenbach & McKee (1989)
Fe⁺	Verner et al. (1999)	Verner et al. (1999)	Verner et al. (1999)
Fe²⁺ $‡$	Zhang (1996)	Zhang (1996)	Zhang (1996)
Fe³⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Fe⁴⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Fe⁵⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Fe⁶⁺ $‡$	Witthoeft & Badnell (2008)	Witthoeft & Badnell (2008)	Witthoeft & Badnell (2008)
Fe⁸⁺	NIST 2014-09-16	NIST 2014-09-16	Del Zanna et al. (2014b)
	Del Zanna et al. (2014b)	Del Zanna et al. (2014b)
Fe⁹⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Fe¹⁰⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Fe¹²⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Fe¹³⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Fe¹⁴⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Fe¹⁵⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Fe¹⁶⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Fe¹⁷⁺	NIST 2014-09-16	NIST 2014-09-16	baseline

Table 16. Table 9: (Cont.)

Species	Energy	Transition	Collision
Fe¹⁸⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Fe¹⁹⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Fe²⁰⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Fe²¹⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Fe²²⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Fe²³⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Co⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	Storey et al. (2015)
		Storey et al. (2015)
Co²⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	Storey & Sochi (2016)
		Storey & Sochi (2016)
Co⁷⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Co⁹⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Co¹⁰⁺ $‡$	NIST 2013-10-01	NIST 2013-10-01	Pelan & Berrington (1995)
Co¹¹⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Co¹²⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Co¹³⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Co¹⁴⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Co¹⁵⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Co¹⁶⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Co¹⁷⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Co¹⁸⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Co¹⁹⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Co²⁰⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Co²¹⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Co²²⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Co²³⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Co²⁴⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ni $‡$	NIST 2013-10-01	NIST 2013-10-01	Hollenbach & McKee (1989)
Ni⁺	NIST 2013-10-01	NIST 2013-10-01	Cassidy et al. (2011)
Ni²⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Ni³⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Ni⁴⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Ni⁶⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline

Table 17. Table 9: (Cont.)

Species	Energy	Transition	Collision
Ni⁸⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Ni¹⁰⁺	NIST 2014-09-16	NIST 2014-09-16	Del Zanna et al. (2014a)
	Del Zanna et al. (2014a)	Del Zanna et al. (2014a)
Ni¹¹⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ni¹²⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ni¹³⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ni¹⁴⁺	Del Zanna et al. (2014c)	Del Zanna et al. (2014c)	Del Zanna et al. (2014c)
	NIST
Ni¹⁵⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ni¹⁶⁺	Aggarwal et al. (2007)	Aggarwal et al. (2007)	Hudson et al. (2012a)
Ni¹⁷⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ni¹⁸⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ni¹⁹⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ni²⁰⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ni²¹⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ni²²⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ni²³⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ni²⁴⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Ni²⁵⁺	NIST 2014-09-16	NIST 2014-09-16	baseline
Cu $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Cu¹²⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Cu¹³⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Cu¹⁴⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Cu¹⁵⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Cu¹⁶⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Cu¹⁷⁺ $‡$	NIST	Fernández-Menchero et al. (2014b)	Fernández-Menchero et al. (2014b)
	Fernández-Menchero et al. (2014b)
Cu²⁰⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Cu²¹⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Cu²²⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Cu²³⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Cu²⁴⁺ $‡$	NIST 2014-09-16	NIST 2014-09-16	baseline
Zn⁺ $‡$	NIST	Kisielius et al. (2015)	Kisielius et al. (2015)
Zn³⁺ $‡$	NIST		baseline
Zn¹⁸⁺ $‡$	NIST	Fernández-Menchero et al. (2014b)	Fernández-Menchero et al. (2014b)
	Fernández-Menchero et al. (2014b)

Equations26

\frac{n ( i + 1 )}{n ( i )} = \frac{Γ ( i )}{α ( i + 1 ) n _{e}}

\frac{n ( i + 1 )}{n ( i )} = \frac{Γ ( i )}{α ( i + 1 ) n _{e}}

Γ (i) = \int ϕ_{ν} σ_{ν} d ν,

Γ (i) = \int ϕ_{ν} σ_{ν} d ν,

Γ (i) = q (i) n_{e},

Γ (i) = q (i) n_{e},

\frac{n ( i + 1 )}{n ( i )} = \frac{n _{e} q ( i )}{n _{e} α ( i + 1 )} = \frac{q ( i )}{α ( i + 1 )}

\frac{n ( i + 1 )}{n ( i )} = \frac{n _{e} q ( i )}{n _{e} α ( i + 1 )} = \frac{q ( i )}{α ( i + 1 )}

\frac{n ( i + 1 )}{n ( i )} = \frac{g _{e}}{n _{e}} \frac{( 2 π mk T ) ^{3/2}}{h ^{3}} \frac{u ( i + 1 )}{u ( i )} exp (- χ_{i} / k T)

\frac{n ( i + 1 )}{n ( i )} = \frac{g _{e}}{n _{e}} \frac{( 2 π mk T ) ^{3/2}}{h ^{3}} \frac{u ( i + 1 )}{u ( i )} exp (- χ_{i} / k T)

C_{p} = 13.25 \times [0.398 f_{3} (T / Θ_{1}) + 0.602 f_{3} (T / Θ_{2})]

C_{p} = 13.25 \times [0.398 f_{3} (T / Θ_{1}) + 0.602 f_{3} (T / Θ_{2})]

for \leavevmode T < 300 \leavevmode K,

for \leavevmode T < 300 \leavevmode K,

C_{p} = 13.25 - 2035/ T + 288 \times 1 0^{5} / T^{2} exp (- 5680/ T)

C_{p} = 13.25 - 2035/ T + 288 \times 1 0^{5} / T^{2} exp (- 5680/ T)

for \leavevmode T \geq 300 \leavevmode K .

for \leavevmode T \geq 300 \leavevmode K .

f_{n} (x) = \frac{1}{n} \int_{0}^{1} \frac{y ^{n} d y}{exp ( y / x ) - 1} .

f_{n} (x) = \frac{1}{n} \int_{0}^{1} \frac{y ^{n} d y}{exp ( y / x ) - 1} .

Λ_{1} = (n_{l} q_{l, u} - n_{u} q_{u, l}) h ν_{u, l}

Λ_{1} = (n_{l} q_{l, u} - n_{u} q_{u, l}) h ν_{u, l}

Λ_{s p ec i es} = u = 2 \sum N l = 1 \sum l < u (n_{l} q_{l, u} - n_{u} q_{u, l}) h ν_{u, l}

Λ_{s p ec i es} = u = 2 \sum N l = 1 \sum l < u (n_{l} q_{l, u} - n_{u} q_{u, l}) h ν_{u, l}

F = \int d t j (t) w (t),

F = \int d t j (t) w (t),

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Full text

The 2017 Release of Cloudy

G. J. Ferland11affiliation: University of Kentucky, Lexington, USA

M. Chatzikos11affiliationmark:

F. Guzmán11affiliationmark:

M. L. Lykins11affiliationmark:

P. A. M. van Hoof22affiliation: Royal Observatory of Belgium

R. J. R. Williams33affiliation: AWE plc, UK

N. P. Abel44affiliation: University of Cincinnati, USA

N. R. Badnell55affiliation: University of Strathclyde, Glasgow, UK

F. P. Keenan66affiliation: Queen’s University Belfast, Belfast, UK

R. L. Porter77affiliation: University of Georgia, USA

P. C. Stancil77affiliationmark:

Nicholas P. Abel: Department of Physics, University of Cincinnati, 4200 Clermont College Drive, Batavia, Ohio, 45103, USA ([email protected]). N. R. Badnell: Department of Physics, University of Strathclyde, Glasgow, G4 0NG, UK ([email protected]) M. Chatzikos, F. Guzmán, Gary J. Ferland and Matthew L. Lykins: Department of Physics and Astronomy, University of Kentucky, Lexington, KY 40506, USA ([email protected]; [email protected]; [email protected]; [email protected]). Francis P. Keenan:Astrophysics Research Centre, School of Mathematics and Physics, Queen’s University Belfast, Belfast BT7 1NN, Northern Ireland, U.K. ([email protected]) Ryan L. Porter and Phillip Stancil: Department of Physics and Astronomy and Center for Simulational Physics, The University of Georgia, Athens, Georgia 30602-2451, USA ([email protected], [email protected]). Peter A. M. van Hoof: Royal Observatory of Belgium, Ringlaan 3, 1180 Brussels, Belgium ([email protected]). Robin J. R. Williams: AWE plc, Aldermaston, Reading RG7 4PR, UK ([email protected])

Abstract

We describe the 2017 release of the spectral synthesis code Cloudy, summarizing the many improvements to the scope and accuracy of the physics which have been made since the previous release. Exporting the atomic data into external data files has enabled many new large datasets to be incorporated into the code. The use of the complete datasets is not realistic for most calculations, so we describe the limited subset of data used by default, which predicts significantly more lines than the previous release of Cloudy. This version is nevertheless faster than the previous release, as a result of code optimizations. We give examples of the accuracy limits using small models, and the performance requirements of large complete models. We summarize several advances in the H- and He-like iso-electronic sequences and use our complete collisional-radiative models to establish the densities where the coronal and local thermodynamic equilibrium approximations work.

Se presenta la versión 2017 del código de síntesis espectral Cloudy junto con las mejoras de precisión y tratamiento de la física desde la version previa. La exportación de los datos atómicos hacia bases de datos externas ha permitido la incorporación de grandes conjuntos de nuevos datos. El uso completo de estos no es práctico para la mayoría de las simulaciones, describiéndose el subconjunto de datos usado por defecto y que predice un número considerable mayor de líneas que en la versión previa. No obstante, la versión actual es más rápida debido a la optimización del código. Se dan ejemplos de los límites de precisión de los modelos pequeños y de los requisitos de rendimiento para los modelos completos. Se sintetizan avances en los modelos para iones con secuencias isoelectrónicas de H y He y el uso de modelos colisionales-radiativos completos en la determinación de las densidades donde las aproximaciones coronal y de equilibrio termodinámico local funcionan.

\addkeyword

atomic processes \addkeywordgalaxies: active \addkeywordmethods: numerical \addkeywordmolecular processes \addkeywordradiation mechanisms \reviewarticle\SetFirstPage1 \SetYear2017

\RescaleLengths

1.1

\reviewtoc

0.1 Introduction

We introduce the next major version of Cloudy, version C17. Cloudy is a non-local thermodynamic equilibrium (NLTE) spectral synthesis and plasma simulation code designed to simulate astrophysical environments and predict their spectra. The previous version of Cloudy, C13, is described in Ferland et al. (2013), hereafter referred to as C13, while the last major review before C13 was Ferland et al. (1998). These give an overview of the scope and goals for our simulation code. The basic physics is described in Osterbrock & Ferland (2006), hereafter referred to as AGN3. Ferland (2003) goes into some atomic and plasma physics questions with examples of Cloudy applications to photoionized clouds.

A great effort since C13 has gone into moving

Cloudy’s atomic and molecular data into external databases. These external databases make it possible to compute intensities of a great many emission lines. A theme running through this review is the tradeoff between the increased accuracy that comes from including larger and more complete models, and the associated increase in time and memory. For this reason using the full databases is usually not practical. Command-line user options control the size of the various model atoms, ions, and molecules. With all databases fully used the number of lines is increased by well over an order of magnitude, and the default setup predicts significantly more lines than the previous release. Despite the increased number of lines, in its default state C17 is actually faster than C13 because of a good number of optimizations introduced to the code base.

The next sections describe how we incorporate a number of external databases to compute large and complex models of ionic and molecular emission. We then discuss how we determine the ionization and emission of the gas, and its range of validity. Other major changes to the physics and functionality of the code are also reviewed. The external data, with its common user interface and underlying software, make it simple to report such quantities as the column density or population in a particular level of a species, or its spectrum. We give examples of using Cloudy to compute both equilibrium (Lykins et al., 2013; Wang et al., 2014) and non-equilibrium (Chatzikos et al., 2015) cooling. The former occurs if the system has not changed over timescales longer than those required for atomic processes to reach steady state. The latter occurs mainly at temperatures at or below the $10^{5}$ K peak in the cooling function, if the temperature changes too rapidly for the system to come into ionization equilibrium. Another change includes options to remove isotropic continuum sources, such as the CMB, from the spectral predictions.

We do not review those parts of the code, its documentation, user support sites, or web access that have not changed since the release of C13. The C13 review paper remains the primary documentation for those parts of this release.

0.2 Databases

0.2.1 The move to external databases

The greatest effort since C13 has gone into a massive reorganization of our treatment of ions, atoms, or molecules, which we collectively refer to as “species”. Cloudy originally added species models on a case-by-case basis, with the data mixed in with the source. This was a significant maintenance problem since only people with a working knowledge of C++ could update the data. We have largely moved the data into external databases. As much as possible we treat all species with a common code base. New models are added to our Stout database (Lykins et al., 2015), which was designed to present data in formats as close as possible to the original data sources, for ease of maintenance.

These databases make it possible to create very extensive models of line emission that include a very large number of levels, although this comes at the cost of longer computing times. They also provide the flexibility of including far fewer levels, with faster execution time, but with a less realistic representation of the physics. This compromise between speed and precision will be a theme running throughout this review.

There are five distinct databases used to model spectral lines. These are outlined here and in more detail in later sections.

The H-like and He-like iso-electronic sequences

We treat atomic one and two electron systems (except H-) with full collisional radiative models, referred to as CRM (see the review by Ferland & Williams (2016)). These models are described in greater detail in Section 0.3.1 below. The models are complete, are capable of making highly accurate predictions of emission, and go to the interstellar medium (ISM), Local Thermodynamic Equilibrium (LTE) and Strict Thermodynamic Equilibrium (STE) limits when appropriate. As described in Section 0.3.2 below, our models of other ions are not as complete.

The H2 molecule

This, the most common molecule in the universe, is treated as an extensive model introduced in Gargi Shaw’s thesis (Shaw et al., 2005). Improvements are described in greater detail in Section 0.5 below.

Stout, CHIANTI, and LAMDA models

We treat emission from atoms, ions, and molecules (other than those described in the previous sections) using the Stout, CHIANTI, and LAMDA databases. These use a common codebase and are controlled in very similar ways. The H-like and He-like iso sequences, and the H2 model, were created as separate projects and are controlled by a separate set of commands.

For molecules, we use the Leiden Atomic and Molecular Database “LAMDA”111http://www.strw.leidenuniv.nl/~moldata/ (Schöier et al., 2005). Section 0.5.5 below gives more details. For some ions, we use version 7.1.4 of the CHIANTI222http://www.chiantidatabase.org/ database (Dere et al., 1997; Landi et al., 2012), as described by Lykins et al. (2013).

We add new species to our Stout database (Lykins et al., 2015). The data format is designed to be as close as possible to the presentation tables in the original publications. This makes Stout easy to maintain and update. We use NIST energy levels where possible.

The original publications defining the LAMDA and CHIANTI databases should be consulted to find the original references for individual data sources. Our Stout database is constantly updated. Appendix .9 gives a summary and references for the data it uses.

There are many species for which NIST gives level energies and transition probabilities but no collision data are available. For these we use NIST data with collision rates from the g-bar approximation (Burgess & Tully, 1992). We refer to these as “baseline” models in Appendix .9. Lykins et al. (2015) gives further details.

Baseline model wavelengths should be accurate, and the transition probabilities are matched to the energy levels, but the g-bar collision strengths are highly approximate. High-quality collision data are available for most astrophysically important species, as shown in Appendix .9, so baseline models are mainly used for species that are not commonly observed.

There are several considerations to keep in mind if a baseline species is important in a particular application. First, the collision rates are highly approximate, so at low densities the line intensities will be too. If the density if high enough for the levels to be in LTE the predictions will be fine. However, with some effort, the predictions could be improved. First, the OPEN-ADAS data collection333http://open.adas.ac.uk/ does include plane-wave Born or distorted-wave collision rates. These are better than g-bar but the data set is not matched to NIST energies. This matching can be done with some effort, as has been done for Fe II (Verner et al., 1999) and Si II (Laha et al., 2016). Alternatively, members of the atomic physics community could be asked to produce close-coupling collision rates for the astrophysical application.

Cloudy has long included a large and very complete model of Fe II emission which was developed as part of Katya Verner’s thesis (Verner et al., 1999). Modern atomic calculations now routinely provide datasets of similar or larger size, so the current version of Cloudy can create complex emission models of any species with sufficient data. The Verner et al. (1999) Fe II model is now fully integrated into the Stout database.

0.2.2 Species and their names

Cloudy simulates gas ranging from fully ionized to molecular. Nomenclature varies considerably between chemical, atomic, and plasma physics. We have adopted a naming convention that tries to find a middle ground between these different fields.

A particular atom, ion, or molecule is referred to as a “species”. A species is a baryon, and this release of Cloudy has 625 species. Examples are CO, H2, H+, and Fe22+. Species are treated using a common approach, as much as possible. Our naming convention melds a bit of each of these fields because a single set of rules must apply to all species.

•

Species labels are case sensitive, to distinguish between the molecule “CO” and the atom “Co”.

•

At present we do not use “_” to indicate subscripts, or “^” to indicate charge.

•

Molecules are written pretty much as they appear in texts. H2, CO, and H- would be written as “H2”, “CO”, and “H-”.

•

Atoms are the element symbol by itself. Examples are “H” or “He” and not the atomic physics notation H0 or He0.

•

Ions are given by “+” followed by the net charge. Examples are “He+2” or “Fe+22” and not the correct atomic physics notation, He2+ or Fe22+. The latter would clash with notation for molecular ions. “C2+” indicates C ${}_{2}^{+}$ in our notation.

•

We specify isotopes using “^” and the atomic weight placed before the atom to which it refers. For example, “^13CO” is the carbon monoxide isotopologue $\rm{}^{13}CO$ .

Appendix .10 lists our species together with the database used to treat them.

0.2.3 Working with spectral lines

These species may emit a collection of photons which we refer to as a spectrum, although the species and spectrum may be labeled differently. We follow the spectroscopic convention that a spectral line is identified by a label and a wavelength. The next sections discuss how each is specified.

Specifying spectral lines

We follow a modified atomic physics notation for the spectrum. In atomic physics, H I, He II, and C IV are the spectra emitted by H0, He+, and C3+. “H I” indicates a collection of photons while H0 is a baryon. In our notation, we replace the Roman numeral with an integer so we refer to the spectrum as “H 1” and the baryon as “H” in the output. For example, H I $\lambda 4861$ Å, He II $\lambda 4686$ Å, and the C IV $\lambda 1549$ Å doublet would appear in the output as H 1 4861.36, He 2 4686.01, and Blnd 1549.00 (blends are discussed below).

Chemistry does not suffer from this distinction between baryons and spectra so the species label is also the spectroscopic ID. Some examples of molecular lines in the output might include H2O 538.142m, HNC 1652.90m, HCS+ 1755.88m, CO 2600.05m, CO 1300.05m, ^13CO 906.599m. In this context the “m” indicates microns rather than our default angstrom unit.

To summarize, atomic hydrogen would be referenced as “H” while the L $\alpha$ line would be “H 1”. The distinction is important because, depending on whether it is formed by collisional excitation or recombination, L $\alpha$ can trace either H0 or H+.

We continue to follow the spectroscopic convention of denoting a line by its species label and wavelength. This has the problem that several lines in a rich spectrum may have the save wavelength, at least to our quoted precision. An example is the strongest molecular hydrogen line that can be measured from the ground. The H2 1-0 S(1) transition has a wavelength of 2.121 $\mu$ m. However there are a number of H2 lines with nearly this wavelength; 3-2 S(23) 2.11751 $\mu$ m, 1-0 S(1) 2.12125 $\mu$ m, and 3-2 S(4) 2.12739 $\mu$ m. We ameliorate this confusion by reporting the wavelengths with six significant figures in this version. However, this method of identifying lines is fragile and it is still possible that the code will find a line with the specified wavelength that is not the intended target.

Line blends

We introduce the concept of “line blends” in this version. These have the label “Blnd” in the main output, and a simplified wavelength. An example is Blnd 2.12100m, which is the sum of the three H2 lines mentioned above. Operationally, a spectrometer measures the total flux through one spectral resolution element and it is frequently not possible to identify individual contributors to what appears as a single spectral feature. The Blnd output option makes it possible for Cloudy to report what is measured.

There are other cases where spectroscopists report the total intensity of a multiplet even when individual members can be measured. Two examples are the [O ii] $\lambda\lambda 3726,3729$ and [S ii] $\lambda\lambda 6731,6716$ doublets. We report the total multiplet intensity as Blnd 3727 and Blnd 6720. Such multiplet sums had been added to Cloudy on an ad hoc basis in previous versions, often with the label “TOTL”. The “Blnd” entry makes the notation consistent across the code and allows it to be included in the reporting framework described in Section 0.2.6.

Air vs vacuum wavelengths

The convention in spectroscopy, dating back to 19th century experimental atomic physics, is to quote line wavelengths in vacuum for $\lambda<2000$ Å and air wavelengths for $\lambda\geq 2000$ Å. Cloudy has long followed this convention.

There is an increasing trend to use vacuum for all wavelengths, e.g. due to satellite missions and the Sloan project444http://www.sdss.org/dr12/spectro/spectro_basics/. We provide a command, print line vacuum, to use vacuum wavelengths throughout. The continuum reported by the family of save continuum commands, used in several of the examples presented below, is always reported in vacuum wavelengths to avoid a discontinuity at 2000Å.

0.2.4 Which database for which species?

The H-like and He-like iso-electronic sequences are always included, although the default number of levels is a compromise between speed and precision. This is discussed in Section 0.3.1. It is not possible to substitute other models, for instance, CHIANTI, for these species. These iso-sequences are integrated into the ionization-balance solver so are needed for it to function.

The large H2 model is not used by default. It is enabled with the command database H2. In this comprehensive model, radiative/collisional processes are coupled to the dissociation/formation mechanisms and resulting chemistry.

The remaining databases, Stout, CHIANTI, and LAMDA, have different emphases. LAMDA has a focus on molecules and PDRs, while CHIANTI is optimized for solar physics and models in collisional ionization equilibrium. Nonetheless, there are some species that are present in more than one database.

Each database has its own “masterlist” file that specifies which of its models to use. The masterlist file follows the naming convention used within its database. For CHIANTI and Stout, the internal structure of C3+, which produces C IV emission, is called “c_4”. The water molecule in LAMDA is referenced as “H2O”. If a particular species is specified in more than one masterlist file we will use Stout if it exists, then CHIANTI, followed by LAMDA.

A small part of the default Stout masterlist file is shown here:

#c mn_23 #n mn_3 #n mn_4 mn_5 mn_6 #c mn_8 #c mn_9

50 levels for N I to do continuum pumping discussed in

>>referΨ Ferland et al., 2012, ApJ 757, 79

n_1 50 #c n_2 #c n_3 #c n_4 n_5 na_1 na_2 #c na_3 #c na_4

This file lives in the cloudy/data/stout/masterlist directory. Similar files are located in the cloudy/data/chianti/masterlist and cloudy/data/lamda/masterlist directories. Each line in the file has a species label and those beginning with “#” are available but are commented out (we use the “#” character to indicate comment lines across our data files). This shows that we use Stout models of Mn V, Mn VI, N I, Na I, and Na II. We might use CHIANTI data, or ignore, species that are commented out.

It is easy for the user to use species from different databases by editing the masterlist files. But there are consequences of using a non-default database. The biggest is that different databases will often have different versions of the level energies. The line wavelengths may change because we derive the wavelength from the level energies. We use both the line label and the extended precision form of the wavelength to match lines. This may break if the wavelengths change significantly.

We do support changing the databases but in release versions of the code have created MD5 checksums for all of the data included in the download. A caution will be reported if the non-default data files are used. This is intended to remind the user that our default data files have been changed in some way.

0.2.5 How complete a model should be done?

The default setup for the iso-electronic sequences is described below. When our H2 model is selected, the full dataset is used.

The Stout, CHIANTI, and LAMDA databases have similar user interfaces. The default number of levels is described in Lykins et al. (2013). For a particular species, the temperature of maximum ion abundance is hotter in a collisionally-ionized gas than in the photoionized case. Because of this higher kinetic temperature, more levels will be energetically accessible in the collisional case. By default, we use 50 levels for the collisional and 15 levels for the photoionization case.

The number of levels can be adjusted to suit particular needs. There are several ways to do this. The command database species "name" levels xxx will change the number of levels for a particular species. The command database CHIANTI levels maximum will make all of the CHIANTI models as large as possible. Similar commands also work for the Stout and LAMDA databases. Finally, the minimum number of levels for a species can be specified in its masterlist file by entering a number after the species label. For instance, faint optical [N I] lines in H II regions are mainly excited by continuum fluorescence (Ferland et al., 2012). This physics requires that the lowest fifty levels of N I be included. This was done in C13 by explicitly including those levels in the C++ source. In this version we specify this minimum number of levels in the Stout masterlist file. The example portion of the Stout masterlist file given above includes this particular case.

With these databases we predict, by default, more lines in this version of Cloudy than with C13. This actually takes less computer time because of memory and other optimizations described below. Figure 1 compares the density of lines per 1000 ${\rm\thinspace km}\mbox{$ {\rm\thinspace s}^{-1}, $}\,$ velocity interval for C13, given as the black dots, and our default setup for C17, the red dots. Most spectral regions now have more lines, often by up to 50%.

Figure 2 compares the density of lines per velocity in our default setup versus a calculation with the databases made as large as possible. The upper panel presents the “big picture”, the line density over the full spectral range we cover, a frequency of 10 MHz $(\lambda\approx$ 29.98 m – this is approximately the lowest frequency observable through the ionosphere) and an energy of $h\nu=$$7.354\times 10^{6}{\rm\thinspace Ryd}$ $(\approx$ $100{\rm\thinspace MeV}$ ). Few lines are present shortward of $10^{-4}\hbox{$ \mu $m}=1$ Å or longward of 3 $\times 10^{6}$ $\mu$ m= 3m. The lower panel zooms into the mid-spectral region with significant numbers of lines. The red points indicate our default C17 setup. The upper envelope of black points results from making the databases as large as possible.

We will give example input scripts across this document to show how various Figures were produced. The large-database calculation in Figure 2 was created with the following input deck:

table AGN ionization parameter -2 stop zone 1 constant temperature 1e4 K hden 0 database H2 database H-like levels resolved 5 database He-like levels resolved 5 database H-like levels collapsed 200 database He-like levels collapsed 200 database CHIANTI levels maximum database stout levels maximum database LAMDA levels maximum save continuum units microns "mesh.con"

All commands are fully documented in “Hazy 1”, Cloudy’s documentation, which is part of the download. Most commands are unchanged from C13. The spectral energy distribution (SED) is our generic Active Galactic Nucleus (AGN) continuum, with an ionization parameter of $U=10^{-2}$ . The geometry is a single zone with a hydrogen density of $1{\rm\thinspace cm^{-3}}$ and a gas kinetic temperature set to 104 K. These have to be specified to get the code to run and were chosen to do the simplest and fastest calculation. As described here, the database commands are new in C17 and control the behavior of the databases.

The number of lines per spectral bin is one of the items in the file produced by the save continuum command. The width of each continuum bin, or, equivalently, the spectral resolution, can be adjusted in two ways. The default continuum mesh can be changed by a uniform scale factor with the set continuum resolution ... command. This method is used in several simulations presented below to increase the spectral resolution to highlight particular issues in the spectrum. Alternatively, the initialization file continuum_mesh.ini in the data directory can be changed to alter the default continuum mesh. This second method was used to make the C13 and C17 continuum meshes the same, to allow the comparison in Figure 1.

Figure 2 shows that it is possible to predict a very large number of lines, but this comes at great cost. The default C17 calculation took 4.3 s on an Intel Core I7 processor while the calculation using the large databases took 1822 s, roughly half an hour. It would not now be feasible to use the full databases in a realistic calculation in which the temperature solver is used and the cloud has a significant column density so that optical depths are important.

0.2.6 Generating reports

database print

The command database print generates a report listing all species. The following would generate a report for Cloudy in its default setup:

test database print

This command was used to generate Tables 2 and 3 giving the default setup for the one- and two-electron iso-sequences. A small portion of the report for the Stout, CHIANTI, and LAMDA databases follows:

Using LAMDA model SO with 70 levels of 91 available. Using LAMDA model SiC2 with 40 levels of 40 available. Using LAMDA model CS with 31 levels of 31 available. Using LAMDA model C2H with 70 levels of 102 available. Using LAMDA model OH+ with 49 levels of 49 available. Using STOUT spectrum Al 1 (species: Al) with 15 levels of 187 available. Using STOUT spectrum Al 3 (species: Al+2) with 15 levels of 83 available. Using STOUT spectrum Al 4 (species: Al+3) with 15 levels of 115 available. Using STOUT spectrum Zn 2 (species: Zn+) with 15 levels of 27 available. Using STOUT spectrum Zn 4 (species: Zn+3) with 2 levels of 2 available. Using CHIANTI spectrum Al 2 (species: Al+) with 15 experimental energy levels of 20 available. Using CHIANTI spectrum Al 5 (species: Al+4) with 3 experimental energy levels of 3 available. Using CHIANTI spectrum Al 7 (species: Al+6) with 15 experimental energy levels of 15 available. Using CHIANTI spectrum Al 8 (species: Al+7) with 15 experimental energy levels of 20 available. Using CHIANTI spectrum Al 9 (species: Al+8) with 15 experimental energy levels of 54 available.

Each line of output gives the database name, the spectroscopic designation, the species designation, the number of levels used, and the total number available. With CHIANTI there is the further option to use all levels, or only those with experimental (measured) energies.

save species labels all

The save species labels all command will produce a file containing the full list of species labels. One can generate this list by running the following input deck:

test save species labels "test.slab" all

Table 1 shows a small part of the resulting output. There are several important points in this Table. First, several species do not list a database. The cases of “H+”, “He+2”, and “C+6” are bare nuclei and have no electronic levels, while the negative hydrogen ion“H-” and the molecules “HeH+”, “C2+” and “CN+” do have internal levels in nature, but we currently do not have models of these systems. The remainder are treated with one of the databases described above. Although many of these species have no internal structure, other species properties, especially the column density, are computed and reported.

Note a likely source of confusion. As described above, “C+2” is doubly ionized carbon, while “C2+” is an ion of molecular carbon.

print citation

The print citation command reports the ADS links to papers defining the databases we use. We encourage users to cite the original source of any data that played an important role in an investigation. This will support and encourage atomic, molecular, and chemical physicists to continue their valuable work.

save species commands

It is easy to report internal properties of a species, such as the column density or population of a particular level. The following is an example of a save species command used to report the column densities of several species and the visual extinction:

save species column densities "test.col" "e-" "CO[2]" "C[1:5]" "H2" "H" "H+" "*AV" end of species

The total column density of electrons, H2, H0, and H+ would be reported, along with the population in the $J=1$ level of CO, and the first five levels of C0. Note that the level index within the “[xx:yy]” counts from a lowest level of 1.

Save line labels

The save line labels command creates a file listing all spectral-line labels and wavelengths in the same format as they appear in the main output’s emission-line list. This is a useful way to obtain a list of lines to use when looking for a specific line. The file is tab-delimited, with the first column giving the line’s index within the large stack of spectral lines, the second giving the character string that identifies the line in the output, and the third giving the line’s wavelength in any of several units. Each entry ends with a description of the spectral line. Lines derived from databases (CHIANTI, Stout, LAMDA) are followed by a comment that contains the database of origin and the indices of the energy levels, as listed in the original data.

An example of some of its output follows:

4ΨInci 0 ΨΨtotal luminosity in incident continuum 5ΨTotH 0 ΨΨtotal heating, all forms, information since individuals added later 6ΨTotC 0 ΨΨtotal cooling, all forms, information since individuals added later 1259ΨH 1 911.759AΨΨradiative recombination continuum 1260ΨH 1 3646.00AΨΨradiative recombination continuum 1261ΨH 1 3646.00AΨΨradiative recombination continuum 1262ΨH 1 8203.58AΨΨradiative recombination continuum 3552ΨH 1 1215.68AΨΨH-like, 1 3, 1^2S - 2^2P 3557ΨH 1 1025.73AΨΨH-like, 1 5, 1^2S - 3^2P 3562ΨH 1 972.543AΨΨH-like, 1 8, 1^2S - 4^2P 5328ΨCa B 1640.00AΨΨcase a or case b from Hummer & Storey tables 5329ΨCa B 1215.23AΨΨcase a or case b from Hummer & Storey tables 73487ΨCO 2600.05mΨΨLAMDA, 1 2 73492ΨCO 1300.05mΨΨLAMDA, 2 3 73497ΨCO 866.727mΨΨLAMDA, 3 4 85082ΨC 3 1908.73AΨΨStout, 1 3 85087ΨC 3 1906.68AΨΨStout, 1 4 85092ΨC 3 977.020AΨΨStout, 1 5 180217ΨAl 8 5.82933mΨΨCHIANTI, 1 2 180222ΨAl 8 2139.33AΨΨCHIANTI, 1 4 180227ΨAl 8 381.132AΨΨCHIANTI, 1 8 312763ΨH2 1.13242mΨΨdiatoms lines 312768ΨH2 1.26316mΨΨdiatoms lines 312854ΨBlnd 2798.00AΨΨBlend: "Mg 2 2795.53A"+"Mg 2 2802.71A" 312855ΨBlnd 615.000AΨΨBlend: "Mg10 609.794A"+"Mg10 624.943A"

0.3 The ionization equilibrium

Our goal is to compute the ionization for a very wide range of densities and temperatures, as shown in Figures 17 and 18 of C13, for the first thirty elements and all of their ions.555Those figures were created using the output of grid_extreme from our the test suite. This development is complete for the H- and He-like iso-electronic sequences, but is still in progress for many-electron systems. This is challenging because of limitations in the available atomic data, computer hardware, and human effort. We currently use a hybrid scheme, outlined below, which is motivated by the astrophysical problems we wish to solve. Figure 3 shows energy levels for five typical species, to serve as an example of some of the issues involved in the following discussion.

There are two simple limits for the solution of the ionization equilibrium. At the low densities found in the interstellar medium (ISM), most ions are in the ground state and the ionization rate per unit volume is proportional to the atom density multiplied either by the electron density, in the case of collisional ionization, or the ionizing photon flux, in the case of photoionization. The recombination rate per unit volume is proportional to the product of the ion and electron density. The density dependence drops out for the case of collisional ionization (see Equation 4 below). In contrast, at very high densities, such as the lower regions of a stellar atmosphere, the ionization is given by the Saha-Boltzmann equation and is inversely proportional to the electron density (Equation 5 below). Cloudy spans both regions so neither approximation can be made and a full solution of the NLTE equations should be performed to obtain the ionization balance and level populations.

In a collisional-radiative model (CRM), the name given to the full NLTE treatment in plasma physics, the level populations are determined self-consistently with the ionization. That is, the populations in bound levels and the continuum above them in Figure 3 are solved as a coupled system of equations. We use this approach for the H-like and He-like iso-sequences, as described in C13 and the following sections, and shown in Figures 17 and 18 of C13. A modified two-level approximation, described below, is used for other species. This hybrid approach is motivated by the physics of the systems shown in Figure 3.

0.3.1 The H- and He-like iso-electronic sequences

Energy levels for H0 are shown in Figure 3 on the left. This structure is valid for all ions of the H-like isoelectronic sequence. For H0, the first excited $n=2$ level occurs at an energy $E\approx(1-1/n^{2})\approx 0.75$ of the ionization limit. This lowest excited level is much closer to the continuum above it than to the ground state below it. At the low kinetic temperatures found in photoionization equilibrium there should be little collisional coupling between excited and ground states because of this large energy separation, although very highly excited levels are strongly coupled to the continuum above. As a result, the very highly excited “Rydberg” levels are populated following recombination from the ion above it, rather than collisional excitation from the ground state, in photoionization equilibrium. Most optical and infrared emission is produced following recombination in this case. The energies of the $n$ -shells are also roughly valid for the He-like sequence. These two-electron systems behave in a similar manner; optical and IR lines form by recombination.

We treat these two iso-electronic systems with a full CRM because of the strong coupling of excited level populations to the continuum (C13). A large coupled system of equations is solved to determine both the level populations and the ionization, so the two are entirely self-consistent. Predictions over a wide range of density and temperature are shown in figures 17 and 18 of C13. We developed a unified model for both the H-like and He-like isoelectronic sequences, that extends from H to Zn, as described by Bauman et al. (2005); Porter et al. (2007); Porter & Ferland (2007); Porter et al. (2012) and Luridiana et al. (2009), As shown in C13, and previously by Ferland & Rees (1988), our model of the ionization and chemistry of hydrogen does go to the correct limits at high (LTE) and low (ISM) densities, and to the strict thermodynamic equilibrium (STE) limit when exposed to a true blackbody. This is only possible when the ionization and level populations are self-consistently determined by solving the full collisional-radiative problem.

Recent Developments

The H- and He-like isoelectronic-sequences, coupled with the cosmic abundances of the elements, cause their spectra to fall into two very different regimes. For brevity, we refer to these as iso-sequences in the following. Hydrogen and helium have low nuclear charge $Z$ and so have low ionization potentials, $IP\sim Z^{2}$ . As a result, H i, He i, and He ii emission is produced in gas with nebular temperatures, $\sim 10^{4}$ K, and occurs mainly in the optical and infrared. A goal of the current development is the prediction of highly accurate line emissivities as a step towards measuring the primordial helium abundance (Porter et al., 2007).

The next most abundant elements, starting with carbon ( $Z=6$ ), have high ionization potentials, $IP\geq 6^{2}$ Ryd, so are produced in very hot gas, $T\geq 10^{6}$ K, and emit in the X-rays (Porter & Ferland, 2007; Porter et al., 2006). Heavy elements of these iso-sequences fall into very different spectral regimes than hydrogen and helium, probe gas with very different physical conditions, and so are found in distinctly different environments.

The high precision needed for primordial helium measurements means that the atomic data must be quite accurate. We are revisiting this problem. The original papers on H i and He i emission (Brocklehurst, 1971, 1972; Benjamin et al., 1999; Hummer & Storey, 1987) all used the Pengelly & Seaton (1964) theory of $l$ -changing collisions. Vrinceanu & Flannery (2001b, a) and Vrinceanu et al. (2012) present an improved theory for these collisions, which predict rate coefficients that are $\sim$ 6 times smaller. We have used these newer rates in most of our published work (Bauman et al., 2005; Porter et al., 2007; Porter & Ferland, 2007; Porter et al., 2012; Luridiana et al., 2009), and in C13.

There are good reasons, outlined in Guzmán et al. (2016), to prefer the Pengelly & Seaton (1964) theory. Guzmán et al. (2017b) extend this treatment to He-like systems, in which the low- $l$ S, P, and D states are not energy-degenerate, so an extra cut-off energy term is applied to the probability integral as in Pengelly & Seaton (1964). Guzmán et al. (2017b) also improve the Pengelly & Seaton (1964) approximations to deal with low densities and high temperatures, where the original formulae could produce negative values. They call this the modified Pengelly & Seaton (1964, PS-M) approach. Williams et al. (2017) extend the semi-classical theory of Vrinceanu & Flannery (2001b) to provide thermodynamically-consistent $l$ -changing rates, which are found to agree quite closely with the results of the other approaches.

These differences affect the predictions. In Figure 4, the line emissivities from the different approaches are divided by the emissivities obtained using PS-M. Here, a single layer of gas has been considered and emissivities from recombination lines calculated. A cosmic helium abundance, He/H =0.1, was assumed. The cloud is radiated by a monochromatic radiation field using Cloudy’s laser command. It was centered at 2 Ryd, with an ionization parameter of U = 0.1 (Osterbrock & Ferland, 2006). The state of the emitting gas in these conditions resembles that in H ii regions, observationally relevant for the determination of He abundances (Izotov et al., 2014). The monochromatic radiation field was used to prevent internal excitations that would be produced by a broadband incident radiation field. A constant gas kinetic temperature of 1 $\times 10^{4}$ K is assumed. We assume ‘Case B’ (Baker & Menzel, 1938), where Lyman lines with upper shell $n>2$ are assumed to scatter often enough to be degraded into Balmer lines and Ly $\alpha$ . The hydrogen density is varied over a wide range and the electron density is calculated self-consistently. The latter is approximately 10% greater than the hydrogen density since He is singly ionized. The atomic data used for He and H emission, except for $l$ -changing collisions, are the ‘standard’ set of data that has been described in previous work (Porter et al., 2005).

In Figure 4, the most accurate quantum mechanical treatment, VOS12-QM (Vrinceanu & Flannery, 2001b, a; Vrinceanu et al., 2012), agrees closely with PS-M. The semiclassical treatments, VOS12-SC (Vrinceanu & Flannery, 2001b, a), and the further simplified approach, VOS12-SSC (Vrinceanu et al., 2012), produce differences up to 10% in the predicted line intensities. The input to generate Figure 4 for the quantum mechanical case can be as follows666The commands must be written in one line. Here, “\” is used to break the command in two lines for presentation purposes.:

laser 2 ionization parameter -1 hden 4 element helium abundance -1 init file "hheonly.ini" stop zone 1 set dr 0 database he-like resolved levels 30 database he-like collapsed levels 170 database he-like collisions l-mixing S62
no degeneracy thermal VOS12 quantal constant temperature 4 case b no photoionzation no Pdest no scattering escape no induced processes iterate normalise to "He 1" 4471.49A monitor line luminosity "He 1" 7281.35A
-18.8686

In this input, the no photoionization option is added to case b to suppress photoionization from excited levels. Then, the pumping of the levels by the resulting photon field is removed by turning off the destruction of Lyman lines with the no Pdest option. The density is set to $10^{4}\,\text{cm}^{-3}$ using the hden 4 command, and the temperature is kept constant with constant temperature 4. The commands:

stop zone 1 set dr 0

define a layer of gas of 1 cm thickness. The commands

no scattering escape no induced processes

prevent losses due to scattering, so that all Lyman lines are degraded into Balmer lines. The monitor line luminosity … command compares the computed luminosities ( erg s*-1*) of selected He i lines against the reference values given by the PS method. Luminosities can be directly translated into emissivities as the thickness of the gas is 1 cm.

Figure 4 has been computed extending to the $n=200$ shell using the database he-like resolved/collapsed levels commands described in section 0.3.1. Most of the higher $n$ -shells are collapsed (C13, Figure 1) assuming a statistical population for the $l$ -subshells, while the low- $n$ levels are resolved. The number of resolved levels needed is determined by the critical density for $l$ -mixing, where collisional transition rates exceed radiative decay rates, as shown in figure 4 of Pengelly & Seaton (1964). The number of resolved $n$ -shells used to predict the lines in Figure 4 has been varied from $n=60$ at the lowest density to $n=20$ at the highest density.

Commands are provided to select the preferred $l$ -changing theory in the input file for Cloudy. The command to use PS-M is:

database he-like collisions l-mixing S62
no degeneracy Pengelly

The keyword S62 in this command tells Cloudy to use the Seaton (1962) electron impact cross sections for the $l$ -changing collisions of the highly non-degenerate $l<3$ subshells (see Guzmán et al., 2017b). The keyword no degeneracy uses an energy criterion (Pengelly & Seaton, 1964) to account for the non-degeneracy of the $l$ -subshells of He i Rydberg levels (see above). Calculations using the original formulas provided by Pengelly & Seaton (1964) can be used by adding the keyword Classic.

VOS12-QM rate coefficients can be used with the command:

database he-like collisions l-mixing S62
no degeneracy thermal VOS12 Quantal

where the keyword thermal tells Cloudy to perform a Maxwell average for the cross sections. By default, the effective coefficients will not be Maxwell averaged, and energies of the collision particles will be taken to be $kT$ . The evaluation at a single energy $kT$ is significantly faster.

The VOS12-QM theory needs a larger number of operations than the analytic PS-M approach. It also needs a numerical integration of the collision probability to obtain the cross sections. These may be integrated once more to obtain the Maxwell averaged coefficients, making this method computationally slow. Simulations using VOS12-QM calculations are $\sim 60$ times slower than those using PS-M. The computational cost of VOS12-QM calculations makes PS-M method the preferred one. The VOS12-QM method is recommended when very high-precision results are required.

Finally VOS12-SC and VOS12-SSC can be obtained using the commands:

{spverbatim}

database he-like collisions l-mixing S62 thermal Vrinceanu database he-like collisions l-mixing S62 VOS12 semiclassical

respectively. While VOS12-SSC cross-sections can be obtained with an analytical formula, VOS12-SC need a double integration making them as computationally slow as VOS12-QM.

It is not now possible to experimentally determine which of the theories mentioned above is more correct, although we prefer the PS-M approach. Guzmán et al. (2017a) outline an astronomical observation that, while difficult, could conclusively determine which $l$ -changing theory holds.

Adjusting the size of the model

Our models of the H- and He- like iso-sequences have a mixture of resolved and collapsed levels, as shown in Figure 1 of C13. Resolved levels are relatively expensive to compute due to the need to evaluate the $l$ -changing collision rates described above. Collapsed levels assume that the $l$ -levels are populated according to their statistical weight within the $n$ shell, so this expense is avoided.

The number of resolved and collapsed levels are controlled by a family of commands similar to

database H-like hydrogen levels resolved 10 database H-like hydrogen levels collapsed 30 database H-like helium levels resolved 10 database H-like helium levels collapsed 30 database He-like helium levels resolved 10 database He-like helium levels collapsed 30

This example resolves $n\leq 10$ for H i, He i, and He ii and adds 30 collapsed levels to make each atom extend through $n=40$ . These commands work for H-like and He-like ions of all elements up through zinc (Z=30).

The command

database print

generates a report summarizing all databases in use during the current calculation. This includes the number of resolved and collapsed levels for the iso-sequences. By default we resolve $n\leq 10$ with an additional 15 collapsed levels for H i and He ii, and $n\leq 6$ as resolved with an additional 20 collapsed levels for He i.

In C13 and earlier versions the collapsed levels were intended to “top off” the model and their treatment did not have spectroscopic accuracy. Bauman et al. (2005) discuss top off, the need to use a finite number of levels to approximate an infinite-level system, and its effects on predictions. We did not report lines from collapsed levels. A great deal of effort has gone into improving the physics of these levels. Their emission is now highly accurate if the implicit assumption that the $l$ levels within the $n$ shell are populated according to their statistical weight is valid. The densities required for this “well $l$ -mixed” assumption can be derived from Figure 4 of Pengelly & Seaton (1964). We now report emission from collapsed levels up to $n<n_{\rm highest}-4$ . This limit was chosen to avoid the “edge” effects discussed in the next subsection.

Comparisons in the Case B limit

It is possible to judge the accuracy of the predicted lines by comparing with the textbook “Case B” spectrum (Baker & Menzel, 1938). Case B is a well defined limit that is a fair approximation to nebulae (AGN3 Section 4.2) and can serve as an important benchmark. Hummer & Storey (1987) and Storey & Hummer (1995) compute Case B emission and the second paper includes a series of machine-readable tables. We interpolate on these tables to include Case B predictions for H-like ions in the Cloudy output. This makes comparison of our predictions with Case B simple.

While Case B is an idealized limit, it gives a fairly good representation of emission for low to moderate density photoionized nebulae (AGN3). The assumptions break down if Balmer or higher lines become optically thick, when collisional excitation from the ground state becomes important, or if Lyman lines can escape. Optical depth effects become important in high radiation-density environments such as the inner regions of active galactic nuclei. Collisional contributions become important when suprathermal electrons are present as in X-ray ionized neutral gas (Spitzer & Tomasko, 1968), or for hot regions such as very low metallicity nebulae. The Lyman lines may not be optically thick in low column density clouds. These processes are treated self-consistently in any complete Cloudy calculation.

Larger models, with more levels, make the spectrum more accurate, at the cost of longer execution times and higher memory requirements. The default H i model is a compromise between performance and accuracy.

Figure 5 compares our predictions with Storey & Hummer (1995) for a typical “nebular” temperature, $T_{e}=10^{4}$ K, and two densities, $n($ H $)=10^{4}{\rm\thinspace cm^{-3}}$ and $10^{7}{\rm\thinspace cm^{-3}}$ . The lower two panels show predictions of our default model at the two densities while the upper panel shows predictions at the lower density with a greatly increased number of resolved and collapsed levels.

In a normal calculation, Cloudy determines the line optical depths self-consistently, assuming the computed column densities, level populations, and line broadening. A Case B command exists to create these conditions and make these comparisons possible while computing a single “zone”, a thin layer of gas. The command sets the Lyman line optical depth to a large number and suppresses collisional excitation out of $n=2$ , to be consistent with the Storey & Hummer (1995) implementation of Case B. This command was included in the input script used to to create Figure 5. In previous versions of Cloudy we also recommended using the Case B command in certain simple PDR (photodissociation region, or photon-dominated region) calculations to block Lyman line fluorescence. As described in Section 0.5.4 below, we now recommend using a different command in the PDR case, reserving the Case B command for this purpose only.

The large model reproduces the Storey & Hummer (1995) results to high precision. There are differences at the $\sim 2$ % level, which we believe to be caused by recent improvements in the collision and recombination data. This will be the subject of a future paper (Guzmán et al. in preparation).

The middle and lower panels show the results of applying our default H i model ( $n\leq 10$ resolved with an additional 15 collapsed) to the same density, and to a higher density case. The default model was chosen to reproduce the intensities of the brighter H i lines to good precision. The large red filled circles in the middle panel indicate lines with intensities greater than 5% that of H $\beta$ . These have deviations of $\mathrel{\hbox{\hbox to0.0pt{\hbox{\lower 4.0pt\hbox{$ \sim $}}\hss}\hbox{$ < $}}}10\%$ .

The eye picks up a trend for the error ratio to move away from unity along a spectroscopic (that is, Balmer, Paschen, etc) series, as the upper level $n$ increases. This is produced by two effects. The first is an “edge” effect as the upper level approaches the upper limit of the model. (We do not report lines arising from $n\geq n_{\rm highest}-4$ for this reason.) The level populations for the very highest levels are inaccurate because of their proximity to the large “gap” that exists between the highest level and the continuum above. The errors at lower $n$ are due to the fact that the lower collapsed levels, $10<n\leq 15$ , are not actually $l$ -mixed at the lower density of $10^{4}{\rm\thinspace cm^{-3}}$ . The density of $10^{7}{\rm\thinspace cm^{-3}}$ is high enough to $l$ -mix $n=11$ so this model is better behaved. These tests show that the implementation gives reliable results when the number of levels is made large enough.

The default model was designed to reproduce the intensities of the brighter H i lines while being computationally expeditious. As a test we computed the intensities of four of the most commonly observed H i lines over the density and temperature given in the Storey & Hummer (1995) tables including the Case B command. The ratio of our predictions to their Case B values is given in Figure 6. The largest differences are at the higher densities where Case B would not be expected to apply. These differences are due to recent improvements in the H0 collision rates. At the lower densities where Case B might apply the agreement is good; the default model is generally within 10% of Storey & Hummer (1995).

This calculation used our grid command to compute the required range of density and temperature and the save linelist ratio command to save predictions into a file. The input script is

set save prefix "nt" case B hden 4 vary grid 2 14 .5 log constant temperature 4 vary grid 2.7 4.4 0.05 log

stop zone 1 set dr 0 laser 2 ionization parameter 0 init "honly.ini" save grid ".grd" save linelist ratio ".rat" "nt.lines" last no hash

The save linelist ratio command reads the list of lines in the file nt.lines and saves them into the file nt.rat. The list of lines in nt.lines are ordered pairs and the ratio of intensities of the first to second is reported. (The “#” lines are comments added to aid the user and are ignored.) The predictions in the nt.rat file were combined with the grid model parameters saved in the file nt.grd to create the plot. The nt.lines file contained the following set of line ratios:

H 1 4340.49A Ca B 4340.49A

H 1 4861.36A Ca B 4861.36A

H 1 6562.85A Ca B 6562.85A

H 1 1.87511m Ca B 1.87511m

H 1 2.16553m Ca B 2.16553m

Similar tests can be made for other lines of interest.

The number of resolved levels must be increased when higher precision is needed at low densities (Figure 6) or for faint IR / FIR lines (Figure 5). Figure 4 of Pengelly & Seaton (1964) can be used as a guide in deciding how many resolved levels are needed. Their vertical axis is, in effect, the negative log of the hydrogen density. The lines indicate the critical density, defined in eqn 3.30 of AGN3, where $l$ -changing rates are equal to the radiative decay rate. The $l$ levels within the indicated $n$ will be well mixed when the density is significantly higher than this critical density. For instance, at a density of $10^{4}{\rm\thinspace cm^{-3}}$ , the figure shows that the $n=15$ shell is at its critical density. Our default model uses collapsed levels for $11\leq n\leq 15$ , causing the residuals in the center panel of Figure 5. Our lowest collapsed level, $n=11$ , has a critical density of $\sim 10^{5.5}{\rm\thinspace cm^{-3}}$ . This is why the model is so much more accurate, but still not excellent, at the higher density in the lower panel, $10^{7}{\rm\thinspace cm^{-3}}$ .

The user could adjust the model to suit requirements at a particular density using Case B predictions as a guide. We recommend creating a one-zone Case B simulation with a density and temperature set to the conditions under study. Then, adjust the size of the model to achieve the desired accuracy by comparing the lines of interest with the Storey & Hummer (1995) Case B predictions that are included in the output.

Making the model large does come at some cost. Tests show that the test suite example pn_paris, one of the original Meudon meeting test cases (Péquignot, 1986), takes $\sim 27$ s using the default H0 model on a modern Xeon, while the large model in Figure 5 takes 44m 25s.

Convergence of lines onto radiative recombination continua

As mentioned above, the finite size of the H0 model is one source of deviations from Case B line predictions in Figure 5. Any finite model will have a “gap” between the highest level and the continuum above. This gap is also present in the predicted spectrum, as shown in Figure 7. This shows the converging high- $n$ Balmer lines and the Balmer jump corresponding to radiative recombination captures to $n=2$ . The upper panel shows the large model used in Figure 5 while the lower is the default model. There is no “gap” in the large model, or in nature, but rather the Balmer lines merge onto the Balmer jump. This is correct and due to the fact that the oscillator strength is continuous between high- $n$ Balmer lines and the Balmer continuum (Hummer & Storey, 1998). The “gap” in the default model is obvious at this high resolution. We note that Schirmer (2016) presents similar figures.

Lyman absorption lines do not suffer from the complexities of H i emission lines since absorption lines depend on the population of the lower level, the ground state in this case. It is then simple to include an arbitrary number of “extra” Lyman lines, lying above the explicit model, so that “gaps” do not appear. An example is shown in Figure 8, a high-resolution blow-up of the spectral region around the Lyman jump. The calculation used our default H0 model. It is a solar abundance cloud illuminated by our generic AGN SED, with an ionization parameter of $\log U=-2$ , a column density of $N(\mbox{{\rm H}$ {}^{0} $})=10^{18}\leavevmode\nobreak\ \mbox{$ {\rm\thinspace cm}^{-2}, $}$ , and does not assume Case B. The smooth blending into the Lyman jump is the correct behavior. All iso-sequence models are topped off with these extra Lyman lines.

The H- and He-like ions in the X-ray

The entire H-like and He-like series of ions between H I and Zn XXIX are treated with a common code base and have the same commands to change their behavior. Porter & Ferland (2007) discuss the treatment with an emphasis on the changes in the He-like X-ray emission due to UV photoexcitation of the metastable $2\,^{3}$ S level. Mehdipour et al. (2016) compare the X-ray spectral predictions of C13 with the Kaastra (SPEX) and Kallman (XSTAR) codes and find reasonable agreement.

The default number of levels for the H-like and He-like iso-electronic sequences are summarized in Tables 2 and 3. The issues discussed above carry over into the X-ray. Figure 9 shows a small portion of the emission spectrum of a solar abundance cloud photoionized by our generic AGN continuum. The ionization parameter was adjusted to $\log U=10^{0.75}$ to insure that C, N, and O were present as H-like and He-like ions.

The “gap” between the recombination edges and the converging Lyman lines is evident in the upper panel. It is larger than in the H i case because we use relatively few levels for these high-ionization species (see Tables 2 and 3). The number of collapsed levels was increased to predict the spectrum in the lower panel. This larger model was computed with the input stream

set save prefix "large" set continuum resolution 0.1 c only include H, C, N, and O, with an increased c number of collapsed levels database H-like levels collapsed 20 database He-like levels collapsed 20 table agn ionization parameter 0.75 hden 0 stop zone 1 set dr 0 iterate print last iteration save emitted continuum ".con" last units Angstroms

The extra levels produce enough lines to fill in the “gap” at this resolution. Regions of significantly increased emission produced by the additional levels are also evident in the larger model.

0.3.2 A modified two-level approximation for other ions

The two-level approximation

Textbooks on the interstellar medium (ISM), e.g. Spitzer (1978), Tielens (2005), Osterbrock & Ferland (2006), and Draine (2011), write the ionization balance of an ion as the equivalent two-level system:

[TABLE]

where $n(i+1)$ and $n(i)$ are the densities of two adjacent ionization stages, $\alpha(i+1)$ is the total recombination rate coefficient of the ion (cm3 s*-1*) and $\Gamma(i)$ is the ionization rate (s*-1*). In photoionization equilibrium

[TABLE]

where $\phi_{\nu}$ is the flux of ionizing photons [photons s*-1* cm*-2* Hz*-1*], $\sigma_{\nu}$ is the photoionization cross section [cm*-2*] and the integral is over ionizing energies. On the other hand, in collisional ionization equilibrium

[TABLE]

where $q(i)$ is the collisional ionization rate coefficient.

In its simplest form, the two-level approximation assumes that recombinations to all excited states will eventually decay to the ground state, and that all ionizations occur out of the ground state. Only the ionization rate from the ground state and the sum of recombination coefficients to all excited states need be considered, a great savings in data needs.

This two-level approach extends over to the chemistry. Most codes use databases similar to the UMIST Database for Astrochemistry (McElroy et al., 2013). Reactions between complex molecules are treated as a single channel without detailed treatment of internal structure. In this approximation, rate coefficients do not have a strong density dependence and do not depend on the internal level populations of the molecule. Most of the chemical data needed to implement a more complete model simply do not currently exist.

In some fields, the two-level approximation is called the “coronal” approximation when collisional ionization is dominant. The solar corona has low density and is collisionally ionized. The low densities insure that most population is in the ground state and that recombinations to excited states decay to ground. Emission from the solar photosphere is too soft to affect the ionization. Cloudy has long included a coronal command which sets a gas kinetic temperature, informs the code that it is acceptable for no incident radiation field to be specified, and calculates the ionization and emission including thermal collisions and any light or cosmic rays that may also be specified.

The independent ionization / emission approximation

Together with the two-level approximation, we can further assume that emission from low-lying levels are not affected by the ionization / recombination process, so that they can be treated as separate problems. As examples, the C IV $\lambda$ 1549, Mg II $\lambda$ 2978, and [O III] $\lambda\lambda$ 5007, 4959 multiplets are produced by the lowest excited levels of their ions in Figure 3. These levels are much closer to ground than to the continuum, so they should be most directly coupled to the ground state. The fact that, at low particle and photon densities, nearly all of the population of a species is in the ground state, further justifies this assumption.

This “independent ionization / emission” approximation (IIEA) is also suggested from consideration of the relevant timescales. Consider the simple model of the Orion Nebula described by Ferland et al. (2016). The recombination time of a typical ion is $\sim 7$ yr at a density of $n\sim 10^{4}$ cm*-3*. Electrons tend to be captured into highly excited states, which have lifetimes of about 10*-5* s to 10*-8* s, so the electron quickly falls down to the ground state. The electron remains in the ground state for about five hours before another ionizing photon is absorbed and the process starts again.

Line-emission timescales are much faster, with collisional excitation timescales of $\sim 10^{5}$ s at a density of $10^{4}{\rm\thinspace cm^{-3}}$ and photon emission occurring within $\tau\sim 10^{-7}$ s for a typical permitted transition. Collisional / emission processes within the low-lying levels occur on timescales that are $\tau\geq 4$ dex faster than ionization - recombination. As a result, most codes first solve for the ionization distribution of an element, then for the line emission from each ion. They are treated as separate problems.

This is equivalent to the use of photo-emission coefficients (PEC), introduced by Summers et al. (2006) and commonly used in fusion plasmas. The line emissivity for a transition between excited levels $i$ and $j$ can then be expressed as $\text{PEC}_{\sigma,i\to j}^{\text{(exc)}}n_{\sigma}$ , where the excited levels $i$ and $j$ are assumed to be in equilibrium with the ground or metastable state $\sigma$ . Here, $\text{PEC}_{\sigma,i\to j}^{\text{(exc)}}=A_{i\to j}\mathcal{F}_{i\sigma}^{\text{(exc)}}$ is the excitation photo-emission coefficient for the $i\to j$ transition, where $\mathcal{F}_{i\sigma}^{\text{(exc)}}$ accounts for the collisional-radiative excitation to level $i$ from $\sigma$ .

Systems with an especially complex structure, such as Fe II, are a major exception to the discussion so far. Fe II has levels extending, nearly uniformly, between the ground state and the continuum, as shown in the right of Figure 3. The atomic physics of Fe II is especially complex due to the fact that it has a half-filled $\it d$ shell, combined with the near energy degeneracy of the $3d$ and $4s$ electrons. Unfortunately Fe II emission is strong in a number of astrophysically important classes of objects, including quasars and shocked regions. This is a worst case, with our treatment discussed by Verner et al. (1999).

The remainder of this section discusses our implementation of a modified two-level approximation for many-electron systems. The following section discusses our treatment of the bound levels and their emission.

Ionization / recombination rates

Our sources for ionization and recombination data for many-electron systems are summarized in C13. Ground and inner-shell photoionization cross sections are given by Verner et al. (1996), and summed recombination rate coefficients are computed as in Badnell et al. (2003); Badnell (2006) and listed on Badnell’s web site777see http://amdpp.phys.strath.ac.uk/tamoc/RR/, http://amdpp.phys.strath.ac.uk/tamoc/DR/.

We have long used collisional ionization rate coefficients presented by Voronov (1997). Two recent studies, Dere (2007) and Kwon & Savin (2014), have presented new rate coefficients for collisional ionization of some ions. These studies are in very good agreement with Voronov (1997) for temperatures around those of the peak abundance of the ion. Unfortunately, the fitting equations used by Dere (2007) and Kwon & Savin (2014) only return positive values for temperatures around the peak abundance of the ion in collisional ionization equilibrium. The Voronov (1997) fits are well behaved over the full temperature range we cover, 2.7 K to 1010 K.

As described by Lykins et al. (2013), we improved the Voronov (1997) fits by rescaling by the ratio of the new to Voronov values at temperatures near the peak abundance of the ion. This correction was usually small, well less than 20%, so has only modest changes in results.

Collisional suppression of dielectronic recombination (DR)

Dielectronic recombination (DR), a process where a free electron is captured by exciting a bound electron, forming an autoionizing state that can decay into bound levels, is the dominant recombination process for most many-electron ions. We mainly use data from Badnell888http://amdpp.phys.strath.ac.uk/tamoc/DR, which is also the largest collection available for this process.

Energetically, the DR process occurs via levels with energies within kT of the ionization limit, so, as shown in the center of Fig 3, it will mainly populate levels that are close to the ionization limit. Of the ions shown in the Figure, only O iii has low-lying autoionizing levels, the levels above the ionization limit. For many ions, experimental data for such low-lying autoionizing levels are either completely missing or incomplete. The summed DR rates listed on the Badnell web site assume that all these populations eventually decay to the ground state, an approximation that must fail at high densities, as quantified below.

It has long been known that DR is suppressed by collisional ionization at moderate to high densities (Sunyaev & Vainshtein, 1968; Burgess & Summers, 1969; Davidson, 1975). Nikolić et al. (2013) extended previous work to estimate the extent of this suppression for various iso-electronic sequences. Their figure 7 shows that the ionization of low stages of iron can change by nearly 1 dex at densities of $\approx 10^{10}$ cm*-3* when suppression is included. As stressed in that paper, these results are highly approximate, with the uncertainty in the suppression being of order the correction itself. A full solution of the populations of the excited states must be done to get the right answer (Summers et al., 2006). Such corrections do allow the two-level approximation to be applied at higher densities, although with this uncertainty.

Nikolić et al. (2017, in preparation) revisited the problem and estimated new suppression factors that offer an improvement over the 2013 values. We use those suppression factors in this release of Cloudy.

Limits of the two-level approximation for the ionization

Over what density range is the two-level approximation valid? How does it fail outside this range? Despite the wide application of this approximation, we do not know of a discussion of these questions.

The two-level approximation will fail when highly excited states do not decay to the ground, most often due to collisional ionization at high densities. Highly-excited levels are long lived due to smaller spontaneous decay rates; for hydrogenic systems the lifetime scales as $\tau\propto n^{5}$ (He et al., 1990). At the same time, the cross section for collisions increases for higher levels as $n^{4}$ . So, compared to electrons in low levels, those in highly-excited levels decay to lower levels slowly, and a large probability for collisional ionization is the result. Further, at high density and temperature the highly-excited levels of all ions will have significant populations, related to the well-known divergence of the partition function in stellar atmospheres (Hubeny & Mihalas, 2014). Collisional ionization from excited states becomes important, and the use of summed recombination coefficients becomes highly approximate, so both the two-level approximation and “independent ionization / emission” assumptions break down.

We can use our complete CRM solutions for the H- and He-like isoelectronic-sequences to show where the two-level approximation fails. In Figure 10 we consider the ionization of hydrogen in a collisionally-ionized gas at the indicated temperature, shown over a very wide range of density. The Figure, based on one shown in Wang et al. (2014), shows how collisional-radiative effects, mainly involving highly-excited levels, cause the hydrogen ionization to go from coronal equilibrium at low-densities to Local Thermodynamic Equilibrium (LTE) at high densities. Figure 5 of Summers et al. (2006) shows a somewhat similar effect. The kinetic temperature was chosen so that the gas would be partially ionized at the lowest densities, so that a wide range in ionization can result.

In the low-density limit, the solution to the coronal equilibrium two-level system given by Equation 1, is

[TABLE]

where $n(i+1)$ and $n(i)$ are the densities of the ion and atom, and $n_{e}$ is the electron density, which cancels out. The two-level solution is given as the red-dashed line in Figure 10. It does not depend on density since the electron density cancels out when collisional ionization and recombination are in balance. It does have an exponential dependence on temperature, as does the Saha-Boltzmann equation described next, because of the exponential Boltzmann factor that enters in the collisional ionization rate coefficient, $q(i)$ .

In the high-density limit, as might be found in lower parts of some stellar atmospheres, accretion disks near black holes, or certain laboratory plasmas, the gas comes into LTE and the ionization balance is given by the Saha-Boltzmann equation:

[TABLE]

where $g_{e}$ is the electron statistical weight, the $u$ ’s are partition functions, and $\chi_{i}$ is the ionization potential of the atom (Chandrasekhar, 1960; Hubeny & Mihalas, 2014). In this limit, shown as the green dashed-dotted line in Figure 10, the ionization depends exponentially on the temperature and inversely linearly on the electron density. At the microphysical level this can be understood as a balance between collisional ionization, $n(i)+e\rightarrow n(i+1)+2e$ , and three-body recombination, $n(i+1)+2e\rightarrow n(i)+e$ , the inverse process.

The black line in Figure 10 shows the Cloudy collisional-radiative solution. It goes between the coronal approximation, Equation 4, valid at $n\mathrel{\hbox{\hbox to0.0pt{\hbox{\lower 4.0pt\hbox{$ \sim $}}\hss}\hbox{$ < $}}}10^{7}$ cm*-3*, and the Saha-Boltzmann limit at high densities, Equation 5, valid at $n\mathrel{\hbox{\hbox to0.0pt{\hbox{\lower 4.0pt\hbox{$ \sim $}}\hss}\hbox{$ > $}}}10^{16}$ cm*-3*. The density ranges where the coronal, CRM, and LTE limits apply are indicated by the labels.

Many sources, such as the Orion Nebula or planetary nebulae, are safely in the limit where the two-level approximation holds, and lower regions of the solar atmosphere or accretion disks are dense enough to be in LTE. However, many regions, including the emission-line clouds in quasars, or the upper layers of accretion disks, have intermediate densities, where neither approximation holds. This is the density range over which full collisional radiative models must be applied. The density is high enough for collisional ionization from excited levels to be important. Indeed, the modest increase in ionization at densities of $\sim 10^{3}{\rm\thinspace cm^{-3}}$ is caused by collisional ionization from the metastable $2s$ level, which is highly overpopulated due to its small radiative decay rate. At high densities, the ionization increases as the density increases and collisional ionization from more highly-excited levels becomes more important. Eventually, when collisional ionization and three-body recombination comes into balance, the atom comes into LTE (Seaton, 1964).

For more highly-charged ions, the scaling introduced by charge dependencies means their populations also will behave much like Figure 10, but at considerably higher densities. For hydrogenic systems, the lifetime of a level goes as $\tau\propto Z^{-4}$ (Summers et al., 2006), and the energy $\chi$ , to ionize a level $n$ , varies as $Z^{2}$ . Because of this, we expect the two-level approximation to apply at higher densities for more highly-charged ions.

We quantify this in Figure 11. This shows a plot of the ratio of densities of the fully-ionized to single-electron ions that is very similar to Figure 10, but for different elements, indicated by the nuclear charge $Z$ . Here, the $Z^{7}$ scaling of density effects can be seen on the shift in peak densities on going from $Z=2$ to 10. For each element we have chosen a temperature so that the ratio of ion densities at the low-density limit is $\sim$ 1–5.

The Cloudy test suite includes a series of example programs which drive Cloudy in various applications. One such test, collion, creates tables giving the ionization balance in the low-density collisional-ionization limit. These are formatted to be very similar to the tables in Carole Jordan’s classic paper (Jordan, 1969). An example, for helium, is given as Table 4. The first column gives the log of the gas kinetic temperature, while the remaining columns give the logs of the fractional abundances of He0, He+, and He2+. Only portions of the full temperature range are shown. Such tables, covering the lightest 30 elements and our full temperature range, are present in the cloudy/tsuite/programs/collion folder. These tables were consulted to choose the kinetic temperature that resulted in the appropriate low-density limit ionization.

A typical input script, for $Z=10$ , is

set save prefix "z10" init "honly.ini" element neon on database H-like levels collapsed 50 database He-like levels collapsed 50 database H-like continuum lowering off database He-like continuum lowering off hden -5 element neon abundance 5 coronal, temperature 6.8 eden 1 vary grid 1 20 .25 stop zone 1 set dr 0 save element neon ".ion" last no hash save grid ".grd" last no hash

This script first “turns off” all elements except hydrogen, to save time, then turns neon, $Z=10$ , back on. Next, the number of collapsed levels is increased to improve the precision of the solution. The database H-like neon and similar commands give the iso-electronic sequence and element to change the treatment of particular species. Neon is given a very large abundance, 105 times that of hydrogen, to prevent H - Ne charge exchange from affecting the ionization balance. This model is not in charge equilibrium since the electron density is set with the eden command. This is used to show one way of driving Cloudy. The grid command was used to vary the electron density over a wide range. We set the temperature to $\log T=6.8$ with the coronal command, to produce the desired ionization at low densities. Table 5, which summarizes results, lists the chosen temperature for each charge. The model is in collisional ionization equilibrium so the temperature is, in the simplest case, the only important parameter. The two save commands produce files that were later used to create the Figures. Continuum lowering is treated following Bautista & Kallman (2000) and is disabled with the command database H-like continuum lowering off.

The shift of the CRM peak to higher densities for higher $Z$ is dramatic, and is a result of the changing lifetimes, level energies, and collisional rate coefficients, as described above. Table 5 indicates approximate density limits over which the coronal and LTE limits apply. We choose the lower limit where the ratio was higher by $\sim$ 30% than the value at the lowest density. The LTE limit was chosen as the density when the solution goes over to the asymptotic Saha-Boltzmann power-law. The actual results do depend on the chosen temperature. The values in the Table are highly approximate and only intended to serve as a guide.

The photoionization case behaves quite differently from the collisional case. In collisionally-ionized plasmas the peak abundance of an ion with ionization potential IP occurs at temperatures roughly equal to the IP (Jordan, 1969). At these temperatures, collisions can fully populate all levels from the ground state (see Figure 3) so the populations of excited levels are far larger than for a photoionized plasma. Collisional ionization out of these levels produces the CRM peak seen in Figures 10 and 11.

We computed a series of photoionization models for the same elements, density range, and ionization ratio, with results shown in Figure 12. The gas is illuminated by a blackbody with a temperature $T_{BB}=2\times 10^{4}\times Z^{2}$ K. The ionization parameter (AGN3, eqn 14.7) was adjusted to establish the desired ionization ratio in the low-density limit. The gas kinetic temperature, which plays a minor role in establishing the ionization, was set to values typical of the ionization parameter.

The input scripts that produced this figure were similar to

set save prefix "z10" init "honly.ini" element hydrogen ionization -9 0 element neon on element neon abundance -2 hden -5 vary grid 1 20 .25 constant temperature 2e5 K blackbody 2e6 ionization parameter 0.3 database H-like neon levels collapsed 50 database He-like neon levels collapsed 50 database H-like continuum lowering off database He-like continuum lowering off stop zone 1 set dr 0 save element neon ".ion" last no hash save grid ".grd" last no hash

Many of the commands are similar to the collisional ionization case. This example shows a second way to vary the electron density over the desired range. Only H and Ne are included in the model, and Ne has a modest abundance. The hydrogen ionization fraction is set so that the H0 density is $10^{-9}$ smaller than the total hydrogen density.

The CRM peak is not present because excited levels are not overpopulated at these lower temperatures. The decreasing ionization at high densities is produced by three-body recombination bringing higher levels into LTE. Table 6 gives the density limit for the two-level approximation to be valid. This is taken as the density where the ionization ratio has fallen by 30%. As also happened in Table 5, hydrogen behaves differently because electron - atom collision rate coefficients are so much smaller than electron-ion rate coefficients.

Tables 5 and 6 are meant as an indication of where our methods of solving for the ionization of many-electron systems will fail. The ranges of validity are highly approximate for several reasons. The collisional ionization results are sensitive to the gas kinetic temperature while the photoionization predictions depend on the SED shape. Further, we only have compete CRM models, extending to high Rydberg levels, for one and two-electron systems. The energy level structure, as shown in Figure 3, does not resemble the structure of many-electron systems, which tend to have a higher density of levels with low energies.

We are working to incorporate the CRM methods developed by the laboratory plasma community to have a better representation of the highly excited levels. A number of physics codes have been developed, mainly in support of experimental facilities, to do just that. Many are summarized in the NLTE7 and NLTE9 code-comparison workshop summaries of Chung et al. (2013) and Piron et al. (2017). Few of these codes are openly available. The ADAS data collection, part of the ADAS code described by Summers et al. (2006), is available through the OPEN-ADAS999http://open.adas.ac.uk/ portal, and the FLYCHK code of Chung et al. (2005) is hosted at NIST101010http://nlte.nist.gov/FLY/.

0.4 Atoms and ions

The greatest changes to our treatment of atoms and ions involved the move to large databases, as described in Section 0.2, and the development of our Stout database (Lykins et al., 2015). This section describes other advances, mainly involving Gaunt factors.

Gaunt factors, named after John Arthur Gaunt, are corrections to classical electron-ion interaction theory to incorporate quantum results (Garstang, 1993). Three Gaunt factors are encountered (Menzel & Pekeris, 1935; Aller, 1963). Following the Menzel & Pekeris (1935) numbering and Chandrasekhar’s notation (Chandrasekhar, 1957), $g_{I}$ corrects bound-bound transitions, $g_{II}$ describes free-bound processes and is discussed in Section 0.4.2 below, while $g_{III}$ adjusts free-free or bremsstrahlung emission and is discussed in the next Section. Burgess & Summers (1987) summarize numerical methods of calculating radiative Gaunt factors for complex ions.

0.4.1 Free-free Gaunt factor $g_{III}$

Free-free or bremsstrahlung emission is the dominant coolant at high temperatures, is usually the main contributor to radio emission in nebulae, and can produce emission across the full electromagnetic spectrum if the gas is hot enough. As figure 5.2 of Rybicki & Lightman (1979) shows, different ranges of temperatures and photon energies require different asymptotic expressions for $g_{III}$ . These approximations do not join continuously with one another. For this reason, it is not possible to use such expansions across the full temperature and spectral range we cover with Cloudy.

van Hoof et al. (2014) derived averaged and wavelength-specific free-free Gaunt factors $g_{III}$ , limited to non-relativistic energies. This work was extended by van Hoof et al. (2015) to include parameters where relativistic effects were important. Results from both studies were presented as tables that can be downloaded, and these are now used by Cloudy to predict free-free emission.

Values of $g_{III}$ covering the full range of kinetic temperature, photon energy, and nuclear charge considered by Cloudy are shown in Figure 13. As is well known, $g_{III}\sim 1$ across “nebular” temperatures and UV, optical, and NIR wavelengths. The closeness to unity shows that emission is roughly classical over these parameters. We see that $g_{III}$ initially drops below 1 towards shorter wavelengths. But then, at yet shorter wavelengths, it starts rising towards infinity when the relativistic effects become important. We also see that $g_{III}>1$ at long wavelengths, which is the well-known non-relativistic result for radio emission. The values depend on nuclear charge, as shown by comparing the left panel, for hydrogen ( $Z=1$ ), and the right panel, for zinc ( $Z=30$ ). This is because the relativistic effects break the simple $Z$ -scaling that exists in non-relativistic calculations. As explained in the original papers, we believe that these values are highly accurate.

0.4.2 Free-bound Gaunt factors $g_{II}$

Rate coefficients for radiative recombination are derived from the photoionization cross section, using detailed balance, often called the Milne relation (see, for instance, Appendix 2 of AGN3). The $g_{II}$ factor allows classical expressions for the photoionization cross section to be used to obtain accurate recombination rate coefficients.

Seaton (1959), hereafter S59, derived hydrogenic radiative recombination rate coefficients over a broad range of temperature using the $g_{II}$ approach. Sutherland & Dopita (1993), hereafter SD93, claimed that this work was numerically flawed and offered corrections to Seaton’s theory. One of us has revisited the theory of hydrogenic radiative recombination twice (Ferland, 1980; Ferland et al., 1992), referred to as F80 and F92, and found only excellent agreement with S59, so this claim is puzzling.

We use a finite model to approximate the internal structure of H0, so we need to “top off” the model by accounting for the difference between the infinite-level total radiative recombination and the summed recombination to the modeled levels. This requires accurate Case B recombination coefficients over a very broad temperature range. This brings up the SD93 correction to the S59 theory. SD93 is very highly cited, and its results are widely used, so it is important to understand whether there is a problem in S59.

S59 developed analytical expansions, in the small- and high-temperature limits, of quantities that enter into $g_{II}$ . No analytical expansion was possible at intermediate energies and direct numerical integration was performed instead. S59 presented the expansions as equations and the numerical results as tables. S59 estimated the errors due to the high-temperature expansion to not exceed 2% for $T\leq 10^{4}$ K, and that it increased for higher temperatures.

SD93, section 3.4.1 and Figure 1, report that the S59 intermediate-temperature numerical, and high-temperature analytical, results do not smoothly join one another, and offer an alternative expression for the high-temperature part. We show results from their tables and figure as our Figures 14 and 15. Figure 14 shows one of the S59 intermediate results and taken from their tables. The line marked “SD93 Seaton” is their version of S59 while “corrected Seaton” is their altered version. F80 closely followed S59, so we have some experience in working with this paper. The heavy line is our evaluation of S59. It is continuous and somewhat displaced from SD93.

Figure 15 compares total recombination rate coefficients over the narrow temperature range where SD93 report problems. The discontinuities are obvious. The heavy line is our evaluation of S59. It is continuous and displaced from SD93.

Figure 16 shows total recombination rate coefficients over a broad range of temperature. The heavy line gives our reevaluation of the Seaton results. S59 notes that his expressions are not valid above 106 K and the heavy line ends at that temperature. The lines marked “Mappings I” and “corrected Seaton” are taken from SD93. The line marked “Mappings I” is an extrapolation of the S59 expression as used by Arnaud & Rothenflug (1985) and the Mappings I code. The line marked “corrected Seaton” is the expression proposed by SD93.

Today the $g_{II}$ approach is seldom used because large repositories of photoionization cross-sections, such as the Opacity Project (Seaton, 1987), are readily available, so the Milne relation can be applied directly to photoionization cross sections. F92 took this approach. The F92 results are believed to be of high accuracy for all temperatures presented, as was recently confirmed by Mao et al. (2017). S59 reported that his results should be “in error by less than 0.5 per cent for $(T/Z^{2})\leq 10^{5}$ K, by 3 per cent for $(T/Z^{2})=10^{6}$ K and by 31 per cent for $(T/Z^{2})=5\times 10^{6}$ K.” F92 confirms these error estimates.

Our models of the H- and He-like isoelectronic sequences require level-resolved recombination rate coefficients and photoionization cross sections. We now use hydrogenic expressions, for H-like, or results from the Opacity Project as described by Porter et al. (2012), to obtain the needed data. But we use F92 to provide the correction needed to “top off” the hydrogenic systems. We believe that this is the correct approach and do not use the SD93 theory.

0.5 The grain and chemical models

Our treatment of the chemistry and molecular emission is described in C13, Abel et al. (2005), Shaw et al. (2005), and Abel et al. (2008). Grain physics, which has a great impact on the chemistry, is described by van Hoof et al. (2004), while Abel et al. (2005) outlines how we do ionization / recombination of ions on grain surfaces.

0.5.1 H2 formation on grain surfaces

The treatment of H2 formation via catalysis on grain surfaces due to chemisorption and physisorption, the Eley - Rideal mechanism (Le Bourlot et al., 2012), has been modified since C13. Our revised treatment incorporates the rates from Cazaux & Tielens (2002); Cazaux & Tielens (2004), including the corrections of Cazaux & Tielens (2010). Our implementation of the Cazaux & Tielens rates utilizes equations C1, C2, and C3 given in the appendix of Röllig et al. (2013). However, while comparing the latter paper to the Cazaux & Tielens work, two typesetting errors in equation C2, which describes chemisorption, were found, as confirmed by private communications with the authors. First, the entire right side of equation C2, not just the 2F term, should be raised to the -1 power. The second error in C2 involves the exponential dependence of the H2 formation rate on the desorption energy of chemisorbed hydrogen, which should have a negative sign.

The new rates were first used in the chemical modeling of Orion’s Veil (Abel et al., 2016), a low H2 abundance environment where the revised rates were expected to have the greatest impact through increased H2 formation (Le Bourlot et al., 2012). However, since most of the increased rate occurs on PAHs (polycyclic aromatic hydrocarbons, Röllig et al., 2013) and since small grains are largely absent in the Veil, the effects on the H2 abundances were found to be less than 10%.

0.5.2 H2 collisions and H2, HD cooling

Our complete H2 model is described by Shaw et al. (2005). We include three datasets for H2 – H0 collisions, as controlled by options on the command database H2 collisions. The default is to use the rates given by Wrathmall et al. (2007). The rates given by Le Bourlot et al. (1999) are also available. The recent dataset, Lique (2015), which includes ortho-para-changing reactive collisions, is available in this release although Wrathmall et al. (2007) remains the default. The H2 cooling is the explicit sum of the difference between the collisional excitation and deexcitation rates given in Equation 7 below.

We fall back on simple and approximate models of H2 formation and cooling when our complete H2 model is not used. Our model of the dissociation physics is inspired by the Tielens & Hollenbach (1985) H2 model and is described by Elwert et al. (2005). H2 cooling is approximated with the function given by Glover & Abel (2008). HD cooling is given by Flower et al. (2000). There are cases where these are significantly different from the approximations used in C13.

0.5.3 How does the PAH abundance vary across the H+, H0, and H2 regions of a nebula?

Cloudy can consider the adjacent H+, H0, and H2 regions of a nebula, the so-called H ii region, PDR, and molecular cloud. This brings in questions concerning how the PAH abundance changes across these regions. Observations of the Orion Bar originally showed (Sellgren et al., 1990) that PAHs emit in a narrow region that might most closely be associated with the H0 region (AGN3 Section 8.5). The obvious interpretation is that PAHs are destroyed in the H+ region of the nebula. The lack of PAH emission at deeper, more molecular, regions of the PDR could be due to a lack of PAHs in H2 regions, or simply an illumination effect, where PAHs do not fluoresce because UV light does not penetrate into the well-shielded high AV regions where H2 is found. This is an area of active research

We provide three options to describe how the PAH abundance varies with physical conditions. These are controlled with the commands

set PAH constant set PAH "H" set PAH "H,H2"

The constant option will keep the PAH abundance constant across the H+, H0, and H2 regions. This allows PAHs to exist in the H+ region. We do this for completeness, even though observations suggest that PAHs are not present there. The "H" option will scale the PAH abundance as the atomic hydrogen fraction, $n({\rm PAH})\propto n({\rm H})/n_{\rm total}$ , where $n_{\rm total}$ is the total density of hydrogen in all forms. This is the simplest interpretation of the Orion Bar observations. The "H,H2" option will scale the PAH abundance as the fraction of hydrogen that is either atomic or molecular, $n({\rm PAH})\propto[n({\rm H})+2n({\rm H}_{2}))]/n_{\rm total}$ . This is consistent with the Orion Bar observations if UV extinction prevents starlight fluorescence of PAHs in deeper molecular regions. By default, we assume the "H" case.

Figure 17 explores the effects that the PAH abundance has on the electron density in the F1 PDR model of the Lorentz Center workshop on PDRs (Röllig et al., 2007). The model is discussed extensively in that paper, so only the relevant details are given here. We added PAHs with three times our default abundance, to be consistent with current estimates, and computed the three PAH abundance cases. The panels show the abundances of some very important species as a function of extinction AV in the lower panel. Extinction depends on whether the light source is a point source or extended (AGN3 Section 7.6) and we use the point-source definition here.

The main regions of a PDR are evident in the lower panel of Figure 17, which is for the "H" case. Hydrogen is atomic at very shallow regions but quickly becomes molecular. Carbon transitions from C+ to CO in a narrow range around AV $\approx 2-4$ mag. Röllig et al. (2007) give further details. The SED used in this model is only defined over a narrow spectral range, see Figure 18, and includes no H-ionizing photons, so the only H+ present is created by cosmic ray secondary ionization. The next subsection describes some effects this simple SED has our full H i model.

The upper panel shows the predicted electron density. Cosmic rays are the dominant source of hydrogen ionization in this cloud while carbon is a dominant electron donor in some regions. Our treatment of electron capture on grain surfaces is described by van Hoof et al. (2004) and Abel et al. (2005). Although we show all three options, there is little H+ in this model, so the constant and "H,H2" cases are identical. The PAH abundance does not vary across Figure 17 in that case. In the "H" case, PAHs are present near the illuminated face but have very low abundance at AV $>1$ mag, where H is mostly H2.

Table 7 shows the logs of some predicted column densities for the "H,H2" and "H" cases. The differences, which can be large, are caused by the changed electron density. These changes suggest a method to determine observationally how the PAH abundance varies with the hydrogen molecular fraction.

0.5.4 Lyman line pumping in PDR models

We participated in the 2004 Leiden PDR meeting where we compared our predictions with codes specifically designed to model PDRs (Röllig et al., 2007). The comparison models employed an SED that was only defined over a very narrow range of wavelengths, as shown by the heavy red line in Figure 18. This uses our table Draine command to enter the galactic background radiation field given by equation 23 of Draine & Bertoldi (1996).

Early in the comparisons we noticed that our calculations predicted a thin moderately-ionized layer of warm gas on the illuminated face of the PDR that was not seen by the other codes. Some investigation revealed that it was due to hydrogen continuum fluorescence through the Lyman lines, followed by photoionization of the metastable $2s$ level. The process, and our method of removing it to allow comparison with conventional PDR codes, is described next.

Our preferred method of generating the local interstellar radiation field is to use the command table ISM. This uses Figure 2 of Black (1987) to represent the unextinguished local interstellar radiation field (see Figure 18). The continuum generated by Cloudy is exactly that given by Black, except that the radiation field between 1 and 4 Ryd is interpolated from the observed or inferred values. Actually it is thought that this part of the radiation field is heavily absorbed by gas in the ISM so that little radiation exists where the dotted line appears, at least in the galactic plane. Such absorption can be introduced with our extinguish command. This SED does not include the cosmic microwave background so that it can be used at any redshift. The CMB command is used to add this component.

Figure 19 shows a closeup of the UV/ FUV portion of the SED with line styles similar to Figure 18. Conventional PDR calculations of, for instance, the Orion PDR, scale up the Draine isotropic radiation field to mimic the SED of the illuminating stars, in this case the Trapezium cluster.

Our preferred method of treating a PDR near a region of active star formation is to include models of the full SED of the central star cluster to model successive H+, H0, and H2 layers (Abel et al., 2005). The higher blue line in Figure 19 shows a portion of the SED of the Trapezium cluster, as implemented with our new table SED "Trapezium.sed" command. There is a strong Lyman jump and significant Lyman continuum radiation. The lower black line shows the SED transmitted through the H+ region and entering the PDR. The stellar continuum is harder than the Draine field, especially at the short wavelengths which cause electron photoejection from grains.

This SED was produced by creating the following model of the Orion Nebula in the style of Baldwin et al. (1991):

set continuum resolution 0.1 set save prefix "full" phi(H) 13.0721 range 0.25 1 table SED "Trapezium.sed" hden 4 abundances Orion grains PAH 3 function stop efrac -2 stop temperature off constant pressure save overview ".ovr" save continuum units microns last ".con"

This script increases our default continuum resolution by a factor of ten, to make the Lyman absorption line contrast, as discussed next, more prominent. Orion grains and our default PAHs, using the function keyword, are included. Note that it was necessary to recompile the grain opacities since the continuum mesh was changed by increasing the resolution. This was done with the compile grains command. The incident SED is the Trapezium cluster with the flux of ionizing photons, set with the phi(H) command, deduced by Baldwin et al. (1991). Their density was used, and the equation of state assumes constant total pressure. The stop temperature off command prevents the code from stopping at the point where the kinetic temperature falls below 4000 K, one of our default stopping criteria. Instead, the calculation stops when the electron fraction, $n_{\rm e}/n_{total}$ , falls below $10^{-2}$ , a point near the illuminated face of the PDR.

Orion grains, as used here, are fairly grey, apparently due to a deficiency of small particles. As a result, the UV portion of the transmitted SED is fainter than the incident SED, but the shape is not otherwise greatly changed. Nearly all radiation shortward of the Lyman limit is extinguished. The increased continuum resolution used in Figure 19 allows Lyman absorption lines formed in the H+ layer to stand out. The vertical bars at the lower left of the Figure mark the positions of the higher Lyman lines, which are strongly in absorption. Because of this Lyman line self-shielding, very little light reaches the PDR in the cores of the Lyman lines.

Conventional PDR calculations use the smooth Draine & Bertoldi (1996) SED shown in Figure 19. We do participate in PDR meetings and include several PDR models in our test suite, so this SED must be used. This SED, when coupled with our full model of H i, produces several unexpected results. Figure 20, computed using our version of the standard Leiden (Röllig et al., 2007) V4 test case, shows the problem. The heavy line shows our predicted temperature and hydrogen ionization as a function of depth from the illuminated face of the PDR. We predict a thin, $\delta r\sim 10^{10}$ cm, layer of warm ionized gas.

Examination revealed that this layer is produced by photoionization of hydrogen by the Balmer continuum. The process is outlined in Figure 21. Radiation in the Lyman lines pumps H i to excite the $np$ levels. Some decay to the metastable $2s$ level while others decay back to $1s$ producing Lyman lines which then scatter within the H0 gas. These eventually decay to produce a Balmer line and populate either $2s$ or $2p$ . The metastable $2s$ level is long lived and can have a significant population. The Balmer continuum photoionizes $2s$ , producing the warm, more ionized, layer. The thickness of the layer is set by the column density needed for the Lyman lines to become self shielded.

In previous versions of Cloudy, we recommended adding the case B command to artificially make the Lyman lines optically thick. That command has other effects and we introduce a new command, database H-like Lyman pumping off, to make all Lyman lines optically thick. The actual effect is to prevent the pumping rather than change the radiative transfer of the Lyman lines. When this command is included in the input deck we obtain the results shown as the light line in Figure 19. This not only makes our predictions more closely match those of conventional PDR codes, but also results in a significant time savings since resolving the thin warm layer is fairly expensive.

0.5.5 The LAMDA database

Since C13, we have updated our version of the LAMDA (Schöier et al., 2005) database of molecular structure and emission. LAMDA does not use version numbers. Our version of LAMDA was downloaded on 2015 June 30.

In addition to updating collisional rates for several species, which have been modified by LAMDA since the last integration of Cloudy with the LAMDA database in C10, we added the ability to predict the intensity of five of their new species: OH+, H2S, H2CS, C2H, and NH2D.

Cloudy uses the stored level energies to predict line wavelengths, photon energies, and collision rates (Lykins et al., 2015). Some of the energy levels present in LAMDA are not precise enough to compute wavelengths with sufficient accuracy. LAMDA does give the frequency of all transitions to higher precision than the energy levels. Therefore, for the species where more precision is needed, we modified the LAMDA data to include higher precision for the energy levels. This was especially important for hyperfine transitions, such as in OH, where computing the wavelengths using the energy level spacing present in LAMDA leads to multiple transitions having the same wavelength. For C17, these issues have been resolved and all hyperfine transitions for OH are now specified with unique wavelengths.

Overall, the improved rates from the LAMDA update did change some molecular intensities in our test suite of simulations. The largest and most notable were H2O, CS, and some of the high rotational transitions of CO.

0.5.6 The grain data

In order to model grains in photoionized environments and XDRs, Cloudy needs grain refractive index data covering the entire wavelength range from the X-ray to the far-IR and sub-mm regime. The reason for this is that Cloudy creates a self-consistent model of the physical state of the grains. The X-ray data are needed because X-ray photons can be important for heating and charging of the grains in environments with a hard radiation field, while the IR and sub-mm data are obviously needed to correctly predict the thermal emission from the grains. Refractive index data covering such a vast wavelength region are very hard to find, which is the reason why Cloudy only offers data for a limited set of grain materials. In C17 we added refractive index data for $\alpha$ -SiC using the results from Laor & Draine (1993). The enthalpy data needed to model stochastic heating of these grains were taken from Chekhovskoy (1971). We created a custom fit to the data from Table 4 for $T<300$ K, while we retained Equation 3 for higher temperatures, resulting in the following formulas for the heat capacity $C_{p}$ of $\alpha$ -SiC in cal mol*-1* K*-1*:

[TABLE]

Here $T$ is the temperature of the grain in kelvin, $\Theta_{i}$ are the Debye temperatures of the material given by $\Theta_{1}=747$ K and $\Theta_{2}=1647$ K, and the function $f_{n}$ is given by:

[TABLE]

To convert the heat capacity to erg mol*-1* K*-1*, the formulas above need to be multiplied by $4.184\times 10^{7}$ erg cal*-1*.

Cloudy includes data for AC and BE amorphous carbon taken from Rouleau & Martin (1991). These data used an erroneous $\beta=2$ extrapolation of the laboratory data towards long wavelengths. This extrapolation has been removed in C17, so that the data now have the expected $\beta\approx 1$ behavior in the sub-mm regime.

The numerical integration scheme for grain opacities that is part of the Mie code has been rewritten, resulting in higher accuracy results. This was needed for the new SiC refractive index data, but will also result in more accurate data for other grain materials. We also modified the code to allow it to converge opacity data for larger grains. All the grain opacity files in C17 have been updated using the new code.

0.6 The Cooling Function

The gas kinetic temperature is the only temperature used in a non-equilibrium plasma. A temperature could be defined for individual level populations or the degree of ionization, but each would have a different value if the gas is not in LTE. The electron velocity distribution in a fully ionized gas is assumed to be Maxwellian because of frequent electron-electron elastic collisions (Spitzer, 1962; Ferland et al., 2016). This is often not true in a partially-ionized gas, especially one exposed to energetic radiation or cosmic rays (Spitzer & Tomasko, 1968). In this case, a population of suprathermal electrons can be present, and is treated as described in C13 and AGN3. The kinetic temperature is set by the energy exchange, the rate at which inelastic collisions between particles convert kinetic energy into light, which then escapes. The cooling function, described here, represents the total cooling due to all species.

0.6.1 Species cooling

Nearly all species are now treated with multi-level models of the internal structure of the atom or molecule. In earlier versions of the code, with much simpler model atoms, it was easy to determine the cooling due to a particular line. For a two-level atom the net energy exchange, the rate at which kinetic energy is converted into light, is

[TABLE]

where the $q$ ’s are collision rates, ${\rm\thinspace s}^{-1}\,$ , $u$ , $l$ indicate the upper and lower levels, and $h\nu_{u,l}$ represents the energy difference, which corresponds to the photon energy for a two-level system.

With a large multi-level model this single-line concept loses meaning. The total cooling due to a species with $N$ levels is

[TABLE]

We now report the total cooling due to a species with the species label followed by a wavelength of “0” to indicate that it represents the entire species. Examples include the following entries which were taken from the output of the save line labels command (Section 0.2.6 above):

30 H 1c 0 net cooling due to iso-seq species 31 H 1h 0 heating due to iso-seq species 32 He 2c 0 net cooling due to iso-seq species 33 He 2h 0 heating due to iso-seq species 450 Sc16c 0 ΨΨnet cooling due to database species 451 Sc16h 0 ΨΨheating due to database species 452 Sc17c 0 ΨΨnet cooling due to database species 453 Sc17h 0 ΨΨheating due to database species

Each entry has either “c” or “h” after the line label. The “c” indicates that the sum in Equation 7 is positive and that the species contributes net cooling. If the level populations are inverted, corresponding to a negative temperature, the species will heat rather than cool the gas, the sum will be negative, and will appear with the “h” character. This often occurs when levels are predominantly photoexcited by the attenuated incident radiation field.

0.6.2 Time-steady non-equilibrium cooling function

Nearly all of the calculations presented in this paper are non-equilibrium in the sense that the ionization and level populations are not described by the gas kinetic temperature. The Boltzmann level population and Saha-Boltzmann ionization equations do not apply. Equilibrium only occurs at the highest densities in Figures 10, 11, and 12.

By default, Cloudy solves for the non-equilibrium conditions in a gas cloud in steady state, i.e., assuming that its properties do not evolve over times that are more rapid than those needed for atomic processes to come into equilibrium. This is accomplished by taking the difference between the recombination and ionization rates in the ionization balance equation for each species to be zero. We provide an age command that will check whether this is a valid assumption.

Gnat & Ferland (2012), Lykins et al. (2013), and Wang et al. (2014) use Cloudy to compute time-steady cooling. Figure 22 shows cooling functions for three very different sets of chemical abundances. The cooling in the primordial case is dominated by H, He, and their molecules, while the solar case has been discussed in many papers. The original review by Dalgarno & McCray (1972) is still among the best. The cooling in the high-metallicity case, marked “Z10”, is dominated by the heavy elements.

The input script used to create the large solar case shown in Figure 22 is given below. This set of calculations uses our grid command to vary the kinetic temperature between 10 K and $10^{10}$ K. The grid and optimize command now use the *nix fork system call to compute a series of independent models, one on each available thread. This method of computing in parallel is discussed further in Section 0.8 below.

set save prefix "all" database H2 database CHIANTI levels maximum database stout levels maximum database lamda levels maximum coronal t=3 vary grid 1 10 .1 log hden 0 stop zone 1 set dr 0 cosmic rays background save grid ".grd" save cooling ".col"

This example may have problems on some computers. A single calculation uses nearly 4GB of RAM since all the models are set to the largest number of levels. However, this is a grid run which launches many models simultaneously. If the total memory requirements of these models exceed the physical memory in the system, the OS may start using swap space (which is very slow) to compute the model, or (if that fails) abort the sim. This is an inevitable consequence of computing many large models on a multi-core system. In such a case, the solution is to run the model sequentially by adding the keyword sequential to the grid command. Then, only a single thread will be started requiring 4 GiB of memory in total. This is another example of the necessary compromise between model fidelity and resource utilization which permeates this review.

The coronal command sets the kinetic temperature and tells the code to bypass its normal check that the incident SED is correctly specified, since a purely collisional model may be intended. The hden command sets the hydrogen density to $1{\rm\thinspace cm^{-3}}$ and the set dr and stop zone 1 commands result in a single zone with a 1 cm thickness. We include the chemistry network originally based on Abel et al. (2005) and Röllig et al. (2007), and originally based on UMIST (Le Teuff et al., 2000). That chemistry will not converge at low temperatures without a source of ionization to drive the ion-molecule reactions. The background cosmic rays provide that ionization.

Figure 22 compares the cooling with both our default and largest data sets, and shows their ratio in the lower panel. The largest differences are due to including the complete H2 model in the large models. The complete model includes detailed treatment of formation and dissociation processes, which results in somewhat different predictions for the chemistry, compared with the default model. The explicit calculation of thousands of H2 lines changes the cooling per H2. Both produce the large differences around 100 K - 1000 K in the primordial case.

There are only small differences at moderate to high temperatures. The default model is surprisingly accurate for these temperatures due to the level selection criteria described by Lykins et al. (2013).

Figure 23 compares the total emission for the “Z10” case and a temperature of $10^{6}$ K for our default (red) and large (black) models. Over much of the spectrum they are similar and the default model includes the strongest lines. However, there are significant “gaps” in some portions of the spectrum as shown in the lowest panel. This shows that our default setup is good enough for most purposes, but that there will be circumstances when a larger model is needed.

0.6.3 Time-dependent non-equilibrium cooling

There are applications where the time-steady assumption is not valid (e.g., Gnat & Sternberg, 2007; Gnat, 2017). Cloudy has long included the option to model time-steady non-equilibrium dynamical flows, (Henney et al., 2005, 2007), in which advected material enters gas at a particular location, altering the balance equations.

Chatzikos et al. (2015) has extended this to treat the time-dependent non-equilibrium case. This extended the steady-state treatment to permit solving the class of time-dependent problems related to the cooling of a parcel of gas from a definite initial state. In the time-dependent scheme, ionization and recombination are not in balance, which introduces a net rate of change for a species density. This permits advancing the ionization for each species over time-steps $\Delta t$ with a first-order implicit integration scheme, as explained in mode detail in Chatzikos et al. (2015).

Example calculations, available as of C17,111111Test suite models time_cool_cd.in and time_cool_cp.in. evolve a unit volume of gas from a preset temperature (30 million K) to a definite floor (10,000 K). The commands

coronal 3e7 init time stop time when temperature falls below 1e4

are used to set the initial and final temperatures.

Figure 24 presents the evolution of temperature with time in the top panel, and of the time-step advance with temperature in the right. The solver adapts the time-step to track the changing conditions in the gas. At high temperatures, the system evolves slowly, and large time-steps are taken, while at lower temperatures, the time-step size is reduced and a larger number of steps is required for fixed fractional change in temperature. In both isochoric and isobaric cases, cooling down to 10 million K overwhelms the total cooling time of the gas, which subsequently cools down to 10,000 K about 4–15 times faster. This is related to the peak of the cooling function around 300,000 K, see Figure 22. For isobaric conditions, the density rises with decreasing temperature, which enhances the cooling rate of the gas, and leads to the shorter cooling time-steps seen in the bottom panel of Figure 24. Compared to the collisional equilibrium ionization case, the system generally takes longer time-steps, because the cooling function is suppressed, as explained in Gnat & Sternberg (2007).

As Figure 25 shows, the time-dependent scheme is able to reproduce the known behavior that deviations from equilibrium at fixed temperature become important only below the peak of the cooling curve at $\sim$ 300,000 K (Chatzikos et al., 2015). We illustrate this effect by forcing equilibrium conditions with the command121212See test suite model time_cool_cp_eq.in.

set dynamics populations equilibrium

In that Figure, the equilibrium ionization fraction of Fe6+ drops quickly with decreasing temperature. This comparison highlights the non-equilibrium origin for the tail to moderate ionization fraction values at lower temperatures.

In time-steady calculations, Cloudy reports the instantaneous intrinsic and emergent emission produced by the cloud, where the latter has been corrected for extinction occurring outside the line-forming region. These are reported by default in the main output of the program.

In time-dependent calculations, integrations over time of the instantaneous intrinsic and emergent emission are performed, referred to as “cumulative” emission. The general form of the integration is

[TABLE]

where $w(t)$ is a weighting function. Its application to line emission is presented in Chatzikos et al. (2015, equation (3)). The integration may be performed in two fashions, or not done at all, through the commands

set cumulative mass set cumulative flux set cumulative off

By default, weighting by mass is performed, which uses a weighting function $w(t)=1/\rho(t)$ , where $\rho(t)$ is the mass density at time $t$ . Notice that the units of the time-integral of the emissivity are erg g*-1*. Obviously then, this choice closely traces the predictions of the cooling flow model (Fabian et al., 1984). Specifically for lines, the integral reduces to the usual line $\Gamma$ factors (Chatzikos et al., 2015; Graney & Sarazin, 1990). Luminosities may be obtained from these estimates by multiplication by the mass cooling rate, $\dot{M}$ . Notice, however, that these considerations apply only to unit volume calculations; for extended clouds the meaning of the computed emission is unclear.

On the other hand, when weighting by flux, the weighting function is taken to be unity, $w(t)=1$ , which suggests that the units of the integrated emission are erg cm*-3*. This is equivalent to the power emitted per unit volume over the course of the integration. For extended models, the units become erg cm*-2*, and have the meaning of the accumulated flux over the duration of the simulation. Such weighting is more appropriate for calculations on fixed domains, as will be discussed in a future paper.

For a cooling flow, we follow a parcel of gas as it cools down, in the manner shown in Figure 24. We integrate over time the intrinsic and emergent emission to capture the emission from all the regions that contribute to the observed spectrum. By default, Cloudy reports both the instantaneous emission from the gas at each time, as well as the cumulative emission up to that point in the simulation, when time-dependent simulations are enabled.

Figure 26 presents the predicted soft X-ray spectrum from a cooling flow, as it would be observed by X-ray telescopes at three different energy resolutions. The bottom panel corresponds to the current state of affairs for X-ray imaging spectroscopy with the detectors onboard Chandra or XMM-Newton. The current state of the art, obtained with X-ray dispersed spectroscopy observations, is shown in the middle panel. Finally, the top panel shows the spectrum that can be obtained with microcalorimeters such as those onboard the unfortunate Hitomi mission, and the proposed Athena mission.

For the purposes of that Figure, the cumulative emission (line and continuum) was obtained by using the keyword cumulative with the family of save continuum commands. Note that the instantaneous cloud emission can still be obtained by invoking the save commands without the cumulative keyword. For reference, a minimum set of commands to reproduce the high resolving power spectrum in the top panel of the Figure is the following

coronal 1.1e7 K init time hden 5.88e-2 linear constant pressure reset set dr 0 set nend 1 stop zone 1 iterate 400 stop time when temperature falls below 6e5 K cosmic rays background set save resolving power 2000 save cumulative continuum units Angstroms
last "cooling.concum"

We point out that the set save resolving power command increases the line-to-continuum contrast without adjusting the line width and therefore violates energy conservation in the saved spectrum.

0.7 Other physics changes

0.7.1 Corrections for isotropic radiation

Chatzikos et al. (2013) expanded our treatment of isotropic radiation fields to produce new reporting options in which their effects are removed, to better approximate what is measured at the telescope. Figure 27 presents an idealized situation, in which an isolated absorber (atom or molecule) is exposed to isotropic radiation, and undergoes two successive excitations, each followed by a radiative deexcitation. An example might be the CMB photo-exciting the CO rotation ladder. Because of the isotropic character of the field, the number of photons scattered out of the line of sight is equal to the number scattered into the line-of-sight, so the radiation field remains isotropic despite the presence of the absorber. No net emission or absorption feature is produced.

A population of absorbers behaves similarly. Isotropic photons at the frequency of an atomic (or molecular) transition are absorbed and reemitted in random directions, leaving the field isotropic. Although pumping by an isotropic continuum does not produce a spectral line in the two-level system, it does affect the level populations, which then affects the line emissivity. This has been traditionally parameterized as a diminution factor on the emergent line intensity (e.g., D’Cruz et al., 1998).

It can be shown that this diminution factor is connected to the escape probability formalism employed for the transfer of radiation. The essence of the escape probability theorem lies in the statement that the net emission from a parcel of gas at some location in a cloud is proportional to the probability of escape from the cloud starting from that location in a single flight. Photons that require several scatterings before escape are assumed to be absorbed in situ, that is, Cloudy does not track their propagation through the gas cloud. The first proof of the theorem was offered by Irons (1978), who showed that it had the character of energy conservation for an emitting volume. The theorem was subsequently extended by Rybicki (1984), who showed that the theorem was far more detailed, as it applied to individual rays. In Chatzikos et al. (2013), Rybicki’s formalism was refined for the case of isotropic radiation, and it was shown that the net emission is proportional to the usual escape probability times a factor that depends on the field intensity (see equations (39) and (40) in Chatzikos et al., 2013). It was also shown that this form reduces to the traditional diminution factor.

Because isotropic radiation may affect line emissivities across the spectrum, as of C17, line intensities are corrected for isotropic radiation by default. This resembles the usual practice in far infrared, millimeter, and radio observations to automatically correct for the CMB by some method (position, beam, or frequency switching), so that the observer is provided with net line or continuum fluxes. This behavior may be disabled with the aid of the command

no lines isotropic continuum subtraction

which causes line intensities uncorrected for isotropic radiation to be reported.

On the other hand, the transmission of the continuum through the cloud involves only the attenuation of radiation at some frequency due to optical depth effects. By default, Cloudy reports the total continuum radiation, including any attenuated isotropic radiation, unless the command

no isotropic continuua report

is issued, or the keyword no isotropic is used with any of the save continuum commands. Either of these options will cause the continuum to be corrected for any (attenuated) isotropic radiation.

As an illustration of these capabilities, Figure 28 presents the emergent spectrum of an interstellar cloud irradiated by two isotropic continua: the cosmic microwave background and the local interstellar continuum. The black and red curves show the total emergent spectrum, and that corrected for isotropic radiation, respectively. The Figure highlights the magnitude of isotropic radiation in the mm, where the CMB overwhelms the diffuse fields by 6–8 dex. The calculation may be reproduced with the following commands:

cmb table ism extinguish 21 cosmic ray background database H2 grains PAH 3 abundances ISM hden 3 stop thickness 1 linear parsec constant temperature 500 K stop temperature off save continuum units microns "iso.con" save continuum units microns no isotropic
"iso.con-noiso"

0.7.2 Chemical composition

The built-in abundance sets have been exported to external files located in the cloudy/data/abundances directory. These are much easier to add or change since updates no longer require editing the C++ source code. There is no change in the abundances-type commands which use these files.

The default composition is given in the file cloudy/data/abundances/default.abn. It can be updated by merely changing this file. A new abundance "filename" command has been introduced. The chemical composition will be read from the specified file, which may be located in the current directory or in cloudy/data/abundances. The abundance "filename" command has a print option to report the gas abundances and grain types used.

The command abundances isotopes may be used to specify isotope fractions for the species of astrophysical interest. By default, the isotope abundances of Asplund et al. (2009) are used, with a couple of modifications (see cloudy/data/abundances/default-iso.abn).

The command element [name] isotopes may be issued to modify the default isotopic abundances to user-specified values. For instance, the commands to specify isotope fractions for hydrogen and carbon are now

element hydrogen isotopes (1, 1) (2, 2e-5) element carbon isotopes (12, 29) (13, 1)

For each element, isotopes are specified as pairs of the atomic weight and its fractional abundance by number. The sum of isotopic abundances is renormalized to unity.

0.7.3 The table SED command and stellar grids

A number of built-in SEDs are available by a series of table ... commands. These have been exported into data files in the cloudy/data/SED directory. This makes the external SEDs simple to maintain and easy to build upon. The table ... commands present in previous versions of the code work as they did before.

The table HM12 command has been added to implement the Haardt & Madau (2012) grid of background continua. The command works the same way as the table HM05 command, except that the keyword quasar is not supported (since data files for the quasar-only case do not exist). This command uses the stellar grid infrastructure described in C13 to do the interpolation. The table HM05 command has also been moved over to use this infrastructure, but this should be transparent to the user.

To implement the new Haardt & Madau grids, a start has been made to convert the stellar grid code so that it can work directly on ascii files without the need to convert them into binary files first. In C17 the following commands are already supported.

table star "somegrid.ascii" <par1> ... table star list "somegrid.ascii"

This concept will be developed further in future releases with the aim to make recompilations of binary files unnecessary when the frequency mesh changes.

0.7.4 Optical depth solution

The total optical depth across the structure is required in order to evaluate escape probabilities from the rear face of the cloud. The algorithm used to update these estimates has been improved, as has the method used to provide fall-back estimates when the optical depth scale has been overrun, that is, the current optical depth is greater than the optical depth in the previous iteration.

One side-effect of the new methods is that localized spikes or troughs can appear in the population densities of species strongly affected by high optical depth transitions, at the position where the optical depth scale is over-run. This may occur if the solution has not been allowed to iterate fully to convergence, and are symptom of a problem which requires further iterations to obtain accurate solutions. The methods used previously would have led to similar inaccuracies in the populations, but with a less obvious pathology.

Overall, these changes have greatly reduced the number of iterations required to converge models with significant line optical depths.

0.8 Other technical changes

0.8.1 The frequency mesh

The method for generating the frequency mesh has been rewritten from scratch. This should solve the long-standing problem where generating a frequency mesh with non-standard parameters would lead to obscure crashes while running the code. Since this change invalidates all existing compiled stellar atmosphere and grain opacity files on its own, we decided to combine it with a reduction of the lower frequency limit to 10 MHz (roughly the lowest frequency LOFAR can observe). We also increased the standard resolving power to 300 and extended the range over which this resolving power is used to $Z^{2}=30^{2}=900$ Ryd, chosen to include all lines of the first thirty elements. As a result, the number of frequency cells has risen from 5277 to 8228 in the standard setup.

0.8.2 Grid runs now fork multicore

In a previous release of Cloudy we added support for parallel execution of model grids using MPI. In this release we added support for parallel execution based on the fork system call. The big advantage of this approach is that no external packages or support libraries are needed. The disadvantage is that this method can only work on a single shared-memory machine. We offer this possibility enabled by default on all UNIX, Mac and Cygwin systems. On UNIX and Cygwin machines, grid runs will run parallel using all available threads unless the user alters this behavior. On a Mac the number is limited to the available physical cores. The number of cores can be adjusted using the grid sequential and grid ncpus options. We will continue to support MPI-based grid runs. This will be the preferred method for very large grids requiring more threads than a single node can offer.

At the end of a grid run, the code will gather the output for each grid point into a single file. For very large grids this can take a substantial amount of time, especially for the main output as these files are already quite large individually. Several options now exist to alleviate this problem. First of all, gathering the main output has been parallelized using MPI I/O. Second, the user now has the option to suppress printing either the intrinsic or emergent line stack (or both) using the

print lines [ intrinsic | emergent ] off

command. This will reduce the size of the main output substantially Last but not least, we have added a keyword separate to the grid command that tells the code to skip gathering of the main output files altogether and leave them as separate files.

0.8.3 Data layout optimizations

Some of the code’s internal data structures have been reorganized to improve locality of data access. Storing numerical data as structures-of-arrays, rather than arrays-of-structures, can dramatically increase the performance of codes on modern CPU architectures, if this results in improvements to the utilization of processor caches. This also offers better opportunities for vectorization. We have also added vectorization primitives for reduction loops and vectorized versions of commonly used math library functions. These changes have led to substantial improvements for test suite problems which emphasize these areas, particularly collisional excitation processes, and worthwhile improvements for more typical cases.

0.8.4 Other changes

A large number of small changes have occurred. These are summarized at http://trac.nublado.org/wiki/NewC17. Some of the more significant changes are:

•

Commas are no longer allowed to be embedded in numbers, they are treated as separators along with most other symbols. Standard exponential format 3.14159e16 can be used instead. Using commas embedded in numbers was deprecated since C10.

•

The tlaw command now has a table option, similar in function to the existing dlaw table command.

•

We are converting the save command output to report linear quantities. The output had reported a mix of log and linear quantities. A log option has been added to report quantities in the old style, for backwards compatibility.

•

The fundamental constants have been updated to the CODATA 2014 values.

•

The run_parallel.pl script can now run only a subset of the scripts in the test suite. We have added a new script rerun_parallel.pl that works the same way as run_parallel.pl, except that it doesn’t delete the output from a previous run.

•

We have added support for platforms where g++ is missing, but the clang++ compiler is present. This includes systems with a FreeBSD or Mac OS X operating system.

0.8.5 Online access

The primary access to all versions of Cloudy remains the http://nublado.org web site. The general organization has not changed since the C13 review. A full set of changes to Cloudy since C13, many too technical to be included here, are on the page http://trac.nublado.org/wiki/NewC17.

Acknowledgments

Many suggestions for improving Cloudy came from the discussion forum hosted at https://groups.yahoo.com/neo/groups/cloudy_simulations/info. Gabriel Altay provided valuable input for implementing the table HM12 command. Testing for the beta versions of this release was provided by Christophe Morriset, Eric Pellegrini, & Mitchell Revalski. We are grateful for their help.

GJF acknowledges support by NSF (1108928; and 1109061), NASA (10-ATP10-0053, 10-ADAP10-0073, and NNX12AH73G), and STScI (HST-AR-12125.01, GO-12560, and HST-GO-12309). MC acknowledges support by NASA (HST-AR-14286.001-A). FG acknowledges support by NSF grant 1412155. PvH was funded by the Belgian Science Policy Office through the ESA Prodex program and contract no. BR/154/PI/MOLPLAN. FPK acknowledges support from the UK Science and Technology Facilities Council.

DEDICATION

We dedicate this paper to the many friends of the Cloudy project across Mexico, and to the Mexican people for making our many visits to their beautiful country so wonderful.

.9 Stout Data

Table 9 lists the species included in the Stout database. Species names followed by the symbol “ $\ddagger$ ” are used by default by Cloudy, as they are listed in Stout.ini masterlist. For each species, the references for the energy, transition probability, and collisional data are given. For the latter, the designation “baseline” has the same meaning as in Lykins et al. (2015), namely that no collisional data are available, and that the “g-bar” approximation is used instead.

The table was prepared with the aid of the Perl scripts cloudy/scripts/db-ref-bib2json.pl and cloudy/scripts/db-ref-json2tex.pl in the Cloudy distribution. The former walks through the Stout database, gathers bibliographical references, and stores them in an external file in JSON format. The latter script operates on the data in that file to create the table itself, in TeX format.

.10 Atomic and Ionic Species Treated in Cloudy

Table 8 lists the species treated by Cloudy, followed by the database from which their energy and transition, radiative and collisional, data are drawn.

The table was prepared with the aid of the cloudy/scripts/db-species-tex.pl Perl script in the Cloudy distribution, which operates on the output of the save species labels command.

Bibliography196

The reference list from the paper itself. Each links out to its DOI / PubMed record.

1Abel et al. (2016) Abel, N. P., Ferland, G. J., O’Dell, C. R., & Troland, T. H. 2016, Ap J, 819, 136
2Abel et al. (2005) Abel, N. P., Ferland, G. J., Shaw, G., & van Hoof, P. A. M. 2005, Ap JS, 161, 65
3Abel et al. (2008) Abel, N. P., Hoof, P. A. M. v., Shaw, G., Ferland, G. J., & Elwert, T. 2008, Ap J, 686, 1125
4Abrahamsson et al. (2007) Abrahamsson, E., Krems, R. V., & Dalgarno, A. 2007, Ap J, 654, 1171
5Aggarwal (1983) Aggarwal, K. M. 1983, Ap JS, 52, 387
6Aggarwal (1985) —. 1985, A&A, 146, 149
7Aggarwal & Keenan (2014) Aggarwal, K. M., & Keenan, F. P. 2014, MNRAS, 442, 388
8Aggarwal et al. (2007) Aggarwal, K. M., Tayal, V., Gupta, G. P., & Keenan, F. P. 2007, Atomic Data and Nuclear Data Tables, 93, 615

TL;DR

Contribution

Findings

Abstract

Peer Reviews

Videos

The 2017 Release of Cloudy

Abstract

0.1 Introduction

0.2 Databases

0.2.1 The move to external databases

The H-like and He-like iso-electronic sequences

The H2 molecule

Stout, CHIANTI, and LAMDA models

0.2.2 Species and their names

0.2.3 Working with spectral lines

Specifying spectral lines

Line blends

Air vs vacuum wavelengths

0.2.4 Which database for which species?

50 levels for N I to do continuum pumping discussed in

>>referΨ Ferland et al., 2012, ApJ 757, 79

0.2.5 How complete a model should be done?

0.2.6 Generating reports

database print

save species labels all

print citation

save species commands

Save line labels

0.3 The ionization equilibrium

0.3.1 The H- and He-like iso-electronic sequences

Recent Developments

Adjusting the size of the model

Comparisons in the Case B limit

Convergence of lines onto radiative recombination continua

The H- and He-like ions in the X-ray

0.3.2 A modified two-level approximation for other ions

The two-level approximation

The independent ionization / emission approximation

Ionization / recombination rates

Collisional suppression of dielectronic recombination (DR)

Limits of the two-level approximation for the ionization

0.4 Atoms and ions

0.4.1 Free-free Gaunt factor gIIIg_{III}gIII​

0.4.2 Free-bound Gaunt factors gIIg_{II}gII​

0.5 The grain and chemical models

0.5.1 H2 formation on grain surfaces

0.5.2 H2 collisions and H2, HD cooling

0.5.3 How does the PAH abundance vary across the H+, H0, and H2 regions of a nebula?

0.5.4 Lyman line pumping in PDR models

0.5.5 The LAMDA database

0.5.6 The grain data

0.6 The Cooling Function

0.6.1 Species cooling

0.6.2 Time-steady non-equilibrium cooling function

0.6.3 Time-dependent non-equilibrium cooling

0.7 Other physics changes

0.7.1 Corrections for isotropic radiation

0.7.2 Chemical composition

0.7.3 The table SED command and stellar grids

0.7.4 Optical depth solution

0.8 Other technical changes

0.8.1 The frequency mesh

0.8.2 Grid runs now fork multicore

0.8.3 Data layout optimizations

0.8.4 Other changes

0.8.5 Online access

Acknowledgments

DEDICATION

.9 Stout Data

.10 Atomic and Ionic Species Treated in Cloudy

0.4.1 Free-free Gaunt factor $g_{III}$

0.4.2 Free-bound Gaunt factors $g_{II}$