Using dust, gas and stellar mass selected samples to probe dust sources and sinks in low metallicity galaxies

TL;DR

This study combines galaxy samples with Herschel data and chemical models to investigate dust sources and sinks in low metallicity galaxies, revealing the importance of grain growth and reduced supernova dust contribution.

Contribution

It introduces a comprehensive analysis of dust evolution in low metallicity galaxies using combined observational data and advanced chemical evolution models, highlighting the roles of inflows, outflows, and grain growth.

Findings

Dust-poor, gas-rich galaxies require delayed star formation histories.

Inflows and outflows are essential to explain metallicities at low gas fractions.

Reduced supernova dust contribution and grain growth are key to matching observed dust masses.

Abstract

We combine samples of nearby galaxies with Herschel photometry selected on their dust, metal, HI, and stellar mass content, and compare these to chemical evolution models in order to discriminate between different dust sources. In a companion paper, we used a HI-selected sample of nearby galaxies to reveal a sub-sample of very gas rich (gas fraction > 80 per cent) sources with dust masses significantly below predictions from simple chemical evolution models, and well below $M_d/M_*$ and $M_d/M_{gas}$ scaling relations seen in dust and stellar-selected samples of local galaxies. We use a chemical evolution model to explain these dust-poor, but gas-rich, sources as well as the observed star formation rates (SFRs) and dust-to-gas ratios. We find that (i) a delayed star formation history is required to model the observed SFRs; (ii) inflows and outflows are required to model the observed metallicities at low gas fractions; (iii) a reduced contribution of dust from supernovae (SNe) is needed to explain the dust-poor sources with high gas fractions. These dust-poor, low stellar mass galaxies require a typical core-collapse SN to produce 0.01 - 0.16 $M_{\odot}$ of dust. To match the observed dust masses at lower gas fractions, significant grain growth is required to counteract the reduced contribution from dust in SNe and dust destruction from SN shocks. These findings are statistically robust, though due to intrinsic scatter it is not always possible to find one single model that successfully describes all the data. We also show that the dust-to-metals ratio decreases towards lower metallicity.

Tables

Table 1. Table 1: The average properties for the samples used in this work quoted as the mean ± plus-or-minus \pm standard deviation. Where data is not available for all the sample we quote the number of sources in the brackets. We only show the late type galaxies (LTGs) in the HRS.

Galaxy Sample	log SFR	log $M_{\mathrm{HI}}$	log $M_{\ast }$	log $M_d$	$f_g$
	($M_{\odot }{\mathrm{yr}}^{-1}$)	($M_{\odot }$)	($M_{\odot }$)	($M_{\odot }$)
DGS	$-0.68\pm 0.85$ (45)	$8.57\pm 0.78$ (35)	$8.10\pm 0.99$	$5.12\pm 1.77$	$0.74\pm 0.23$ (35)
HiGH-low	$-1.19\pm 0.52$	$9.02\pm 0.46$	$8.17\pm 0.56$	$5.21\pm 0.97$	$0.87\pm 0.09$
HiGH-high	$-0.07\pm 0.40$	$9.76\pm 0.39$	$9.89\pm 0.65$	$7.12\pm 0.43$	$0.50\pm 0.24$
HRS (LTGs)	$-0.70\pm 0.67$	$8.94\pm 0.56$ (231)	$9.64\pm 0.57$	$6.70\pm 0.54$ (239)	$0.28\pm 0.22$ (231)

Table 2. Table 2: Parameters for the different chemical evolution models used.

Name	IMF	SFH	Reduced SN dust	Destruction	Grain Growth	Inflow	Outflow
Model I	Chabrier	Milky Way	N	N	N	N	N
Model II	Chabrier	Delayed	N	N	N	N	N
Model III	Chabrier	Delayed	N	N	N	N	$1.5\times$ SFR
Model IV	Chabrier	Delayed	$\times 6$	$m_{\mathrm{ISM}}=150$	$ϵ=700$	$1.7\times$ SFR	$1.7\times$ SFR
Model V	Chabrier	Delayed	$\times 12$	$m_{\mathrm{ISM}}=1500$	$ϵ=5000$	$2.5\times$ SFR	$2.5\times$ SFR
Model VI	Chabrier	Delayed/3	$\times 100$	$m_{\mathrm{ISM}}=150$	$ϵ=8000$	$2.5\times$ SFR	$2.5\times$ SFR
Model VII	Chabrier	Bursty	$\times 12$	$m_{\mathrm{ISM}}=150$	$ϵ=12000$	$4.0\times$ SFR	$4.0\times$ SFR

Table 3. Table 3: Average dust-to-metal ratio for the different galaxy samples and metallicity calibrations quoted as mean ± plus-or-minus \pm standard deviation.

Galaxy Sample	Mean $12+\log \left(O/H\right)$			Mean $M_d/M_Z$
	N2	KE08/T04	PG16S	N2	KE08/T04	PG16S
DGS	$8.02\pm 0.28$	$8.34\pm 0.19$	$7.98\pm 0.30$	$-0.72\pm 0.71$	$-0.44\pm 0.33$	$-0.69\pm 0.66$
HiGH-low	$8.17\pm 0.12$	$8.37\pm 0.16$	$7.99\pm 0.18$	$-1.15\pm 0.70$	$-1.38\pm 0.69$	$-0.97\pm 0.70$
HiGH-high	$8.48\pm 0.12$	$8.81\pm 0.16$	$8.36\pm 0.14$	$-0.49\pm 0.43$	$-0.82\pm 0.42$	$-0.37\pm 0.43$
HRS (LTGs)	$8.57\pm 0.15$	$8.75\pm 0.17$	$8.48\pm 0.18$	$-0.20\pm 0.24$	$-0.38\pm 0.25$	$-0.11\pm 0.27$

Table 4. Table 4: The goodness of fit of each Model as expressed by ⟨ χ eff 2 ⟩ delimited-⟨⟩ subscript superscript 𝜒 2 eff \langle\chi^{2}_{\text{eff}}\rangle for the combined HRS+HAPLESS+H i GH sample and for H i GH-low. Results for Figs.  2 ,  3  and  5 are shown for both samples. M g / M ∗ subscript 𝑀 𝑔 subscript 𝑀 M_{g}/M_{*} was used instead of f g subscript 𝑓 𝑔 f_{g} because for f g subscript 𝑓 𝑔 f_{g} the approximation of a Gaussian error probability distribution is not valid.

Galaxy Sample	Combined			HiGH-low
Fig.	2	3	5	2	3	5
y-axis	$M_d/M_{\text{bary}}$	SFR$/M_{\text{bary}}$	$M_d/M_g$	$M_d/M_{\text{bary}}$	SFR$/M_{\text{bary}}$	$M_d/M_g$
x-axis	$M_g/M_{\ast }$	$M_g/M_{\ast }$	$12+\log \left(O/H\right)$	$M_g/M_{\ast }$	$M_g/M_{\ast }$	$12+\log \left(O/H\right)$
Model I	1.90	7.55	2.73	1.28	20.45	1.65
Model II	2.06	3.56	2.80	1.47	2.72	1.68
Model III	0.84	10.45	2.79	1.36	3.01	1.68
Model IV	1.31	3.55	8.64	0.94	2.72	2.38
Model V	1.03	3.53	1.09	0.89	2.72	2.19
Model VI	3.51	6.43	0.41	0.59	3.77	1.45

Table 5. Table 5: Basic properties for the H i -selected H i GH sample ( DV17 ) and dust-selected HAPLESS sample ( C15 ). The check-marks indicate which sample the source is part of (24 sources are included in both samples).

#	Common name	RA	DEC	Distance	log $M_{{}_{\star }}$	log $M_{\text{H}\text{i}}$	log $M_d$	log SFR	$f_g$	HiGH	HAPLESS
		(J2000 deg)	(J2000 deg)	(Mpc)	($M_{\odot }$)	($M_{\odot }$)	($M_{\odot }$)	($M_{\odot }{\mathrm{yr}}^{-1}$)
1	2MASXJ14265308 +0057462	216.72078	0.96285	120.35	9.60${}_{}{}^{+0.13}{}_{-0.07}{}^{}$	9.62	7.26${}_{}{}^{+0.18}{}_{-0.15}{}^{}$	-0.03${}_{}{}^{+0.07}{}_{-0.07}{}^{}$	0.58
2	CGCG014-010	185.08868	0.36769	11.84	7.29${}_{}{}^{+0.13}{}_{-0.12}{}^{}$	8.21	3.48${}_{}{}^{+0.45}{}_{-0.40}{}^{}$	-2.14${}_{}{}^{+0.04}{}_{-0.04}{}^{}$	0.92
3	CGCG014-014	185.27509	0.5519	41.91	8.54${}_{}{}^{+0.17}{}_{-0.14}{}^{}$		6.13${}_{}{}^{+0.48}{}_{-0.52}{}^{}$	-1.18${}_{}{}^{+0.12}{}_{-0.12}{}^{}$
4	CGCG019-003	214.83333	1.16467	42.99	8.55${}_{}{}^{+0.20}{}_{-0.13}{}^{}$		5.78${}_{}{}^{+0.56}{}_{-0.57}{}^{}$	-1.41${}_{}{}^{+0.17}{}_{-0.25}{}^{}$
5	CGCG019-084	220.62268	1.50173	34.63	9.06${}_{}{}^{+0.13}{}_{-0.13}{}^{}$		6.39${}_{}{}^{+0.12}{}_{-0.11}{}^{}$	-0.83${}_{}{}^{+0.07}{}_{-0.08}{}^{}$
6	FGC1412	184.85783	0.21197	11.32	6.94${}_{}{}^{+0.13}{}_{-0.10}{}^{}$	7.85	3.84${}_{}{}^{+0.78}{}_{-0.53}{}^{}$	-2.43${}_{}{}^{+0.14}{}_{-0.17}{}^{}$	0.91
7	IC1010	216.83483	1.02589	118.19	10.82${}_{}{}^{+0.08}{}_{-0.25}{}^{}$	10.55	7.93${}_{}{}^{+0.12}{}_{-0.13}{}^{}$	0.44${}_{}{}^{+0.04}{}_{-0.06}{}^{}$	0.42
8	IC1011	217.01885	1.00607	117.95	10.16${}_{}{}^{+0.13}{}_{-0.07}{}^{}$	9.73	7.41${}_{}{}^{+0.08}{}_{-0.09}{}^{}$	0.60${}_{}{}^{+0.06}{}_{-0.04}{}^{}$	0.33
9	LEDA1241857	222.59576	2.95833	28.64	8.2${}_{}{}^{+0.14}{}_{-0.20}{}^{}$		4.93${}_{}{}^{+0.68}{}_{-0.47}{}^{}$	-1.90${}_{}{}^{+0.12}{}_{-0.31}{}^{}$
10	MGC0066574	219.99969	-0.18714	33.37	7.18${}_{}{}^{+0.20}{}_{-0.21}{}^{}$		5.22${}_{}{}^{+0.55}{}_{-0.66}{}^{}$	-2.28${}_{}{}^{+0.14}{}_{-0.23}{}^{}$
11	MGC0068525	221.3158	-0.1602	29.19	8.68${}_{}{}^{+0.12}{}_{-0.18}{}^{}$		4.50${}_{}{}^{+0.51}{}_{-0.46}{}^{}$	-1.90${}_{}{}^{+0.13}{}_{-0.30}{}^{}$
12	NGC4030	180.09841	-1.10033	29.39	10.88${}_{}{}^{+0.12}{}_{-0.09}{}^{}$	10.17	7.96${}_{}{}^{+0.04}{}_{-0.08}{}^{}$	0.78${}_{}{}^{+0.04}{}_{-0.05}{}^{}$	0.20
13	NGC4030b	180.19873	-0.02333	38.36	8.85${}_{}{}^{+0.16}{}_{-0.14}{}^{}$	9.36	5.64${}_{}{}^{+0.53}{}_{-0.44}{}^{}$	-0.98${}_{}{}^{+0.09}{}_{-0.32}{}^{}$	0.81
14	NGC4202	184.53574	-1.06413	93.20	10.30${}_{}{}^{+0.11}{}_{-0.10}{}^{}$	10.41	7.46${}_{}{}^{+0.07}{}_{-0.06}{}^{}$	0.05${}_{}{}^{+0.06}{}_{-0.17}{}^{}$	0.63
15	NGC5496	212.9082	-1.15909	27.35	9.46${}_{}{}^{+0.14}{}_{-0.05}{}^{}$	10.03	7.12${}_{}{}^{+0.14}{}_{-0.11}{}^{}$	-0.23${}_{}{}^{+0.04}{}_{-0.04}{}^{}$	0.83
16	NGC5584	215.59857	-0.3869	30.21	9.97${}_{}{}^{+0.09}{}_{-0.16}{}^{}$	9.76	7.51${}_{}{}^{+0.07}{}_{-0.10}{}^{}$	0.26${}_{}{}^{+0.04}{}_{-0.04}{}^{}$	0.45
17	NGC5690	219.42	2.29162	32.10	10.38${}_{}{}^{+0.11}{}_{-0.09}{}^{}$	9.90	7.61${}_{}{}^{+0.05}{}_{-0.05}{}^{}$	0.31${}_{}{}^{+0.05}{}_{-0.04}{}^{}$	0.31
18	NGC5691	219.47216	-0.39846	33.35	10.01${}_{}{}^{+0.10}{}_{-0.17}{}^{}$	9.16	6.85${}_{}{}^{+0.07}{}_{-0.04}{}^{}$	-0.06${}_{}{}^{+0.06}{}_{-0.05}{}^{}$	0.16
19	NGC5705	219.95623	-0.71874	31.35	9.33${}_{}{}^{+0.08}{}_{-0.12}{}^{}$	9.77	7.35${}_{}{}^{+0.12}{}_{-0.13}{}^{}$	-0.24${}_{}{}^{+0.04}{}_{-0.04}{}^{}$	0.78
20	NGC5713	220.04759	-0.28933	33.60	10.56${}_{}{}^{+0.14}{}_{-0.11}{}^{}$	10.06	7.54${}_{}{}^{+0.05}{}_{-0.05}{}^{}$	0.72${}_{}{}^{+0.06}{}_{-0.05}{}^{}$	0.29
21	NGC5719	220.23393	-0.31856	30.72	10.79${}_{}{}^{+0.09}{}_{-0.08}{}^{}$	10.07	7.43${}_{}{}^{+0.07}{}_{-0.06}{}^{}$	-0.17${}_{}{}^{+0.04}{}_{-0.06}{}^{}$	0.20
22	NGC5725	220.24298	2.18655	29.42	9.13${}_{}{}^{+0.08}{}_{-0.13}{}^{}$	8.93	6.45${}_{}{}^{+0.19}{}_{-0.19}{}^{}$	-0.65${}_{}{}^{+0.07}{}_{-0.07}{}^{}$	0.46
23	NGC5733	220.69092	-0.35132	30.13	8.89${}_{}{}^{+0.12}{}_{-0.10}{}^{}$		6.47${}_{}{}^{+0.23}{}_{-0.19}{}^{}$	-0.68${}_{}{}^{+0.04}{}_{-0.07}{}^{}$
24	NGC5738	220.98402	1.60435	31.20	9.68${}_{}{}^{+0.14}{}_{-0.12}{}^{}$		4.93${}_{}{}^{+0.36}{}_{-0.34}{}^{}$	-2.2${}_{}{}^{+0.16}{}_{-0.26}{}^{}$
25	NGC5740	221.10171	1.68019	28.08	10.28${}_{}{}^{+0.11}{}_{-0.07}{}^{}$	9.74	7.16${}_{}{}^{+0.07}{}_{-0.07}{}^{}$	-0.05${}_{}{}^{+0.04}{}_{-0.04}{}^{}$	0.27
26	NGC5746	221.23292	1.955	30.76	11.31${}_{}{}^{+0.07}{}_{-0.10}{}^{}$	9.84	8.00${}_{}{}^{+0.07}{}_{-0.07}{}^{}$	-0.41${}_{}{}^{+0.36}{}_{-0.70}{}^{}$	0.04
27	NGC5750	221.54705	-0.2236	31.12	10.59${}_{}{}^{+0.06}{}_{-0.18}{}^{}$	9.08	6.99${}_{}{}^{+0.07}{}_{-0.07}{}^{}$	-0.70${}_{}{}^{+0.08}{}_{-0.14}{}^{}$	0.04
28	PGC037392	178.7696	1.71954	26.69	8.31${}_{}{}^{+0.14}{}_{-0.12}{}^{}$	8.37	5.77${}_{}{}^{+0.46}{}_{-0.53}{}^{}$	-1.18${}_{}{}^{+0.12}{}_{-0.12}{}^{}$	0.60
29	PGC051719	217.15782	0.55312	29.01	8.82${}_{}{}^{+0.11}{}_{-0.13}{}^{}$		6.42${}_{}{}^{+0.20}{}_{-0.17}{}^{}$	-1.1${}_{}{}^{+0.07}{}_{-0.14}{}^{}$
30	PGC052652	221.12776	1.52249	25.67	8.56${}_{}{}^{+0.15}{}_{-0.17}{}^{}$	7.84	5.82${}_{}{}^{+0.40}{}_{-0.40}{}^{}$	-1.64${}_{}{}^{+0.13}{}_{-0.18}{}^{}$	0.20
31	SDSSJ084258.35 +003838.5	130.74318	0.64408	158.93	9.84${}_{}{}^{+0.13}{}_{-0.13}{}^{}$	9.97	7.21${}_{}{}^{+0.40}{}_{-0.41}{}^{}$	-0.03${}_{}{}^{+0.10}{}_{-0.13}{}^{}$	0.64
32	SDSSJ143353.30 +012905.6	218.47167	1.48543	32.99	7.77${}_{}{}^{+0.19}{}_{-0.18}{}^{}$	8.94	4.52${}_{}{}^{+0.73}{}_{-0.63}{}^{}$	-1.69${}_{}{}^{+0.04}{}_{-0.04}{}^{}$	0.95
33	UGC04673	133.967	2.52426	59.73	9.12${}_{}{}^{+0.20}{}_{-0.17}{}^{}$	9.78	7.40${}_{}{}^{+0.23}{}_{-0.27}{}^{}$	-0.24${}_{}{}^{+0.04}{}_{-0.05}{}^{}$	0.86
34	UGC04684	134.17066	0.37591	40.56	9.35${}_{}{}^{+0.14}{}_{-0.15}{}^{}$	9.58	6.70${}_{}{}^{+0.16}{}_{-0.14}{}^{}$	-0.36${}_{}{}^{+0.10}{}_{-0.12}{}^{}$	0.69
35	UGC04996	140.81604	-0.72945	57.25	9.36${}_{}{}^{+0.12}{}_{-0.10}{}^{}$	9.84	7.18${}_{}{}^{+0.21}{}_{-0.19}{}^{}$	-0.17${}_{}{}^{+0.06}{}_{-0.06}{}^{}$	0.80
36	UGC06578	174.153	0.81678	20.42	8.02${}_{}{}^{+0.10}{}_{-0.06}{}^{}$	8.82	5.72${}_{}{}^{+0.32}{}_{-0.29}{}^{}$	-1.02${}_{}{}^{+0.03}{}_{-0.04}{}^{}$	0.89
37	UGC06780	177.20993	-2.03249	34.16	9.00${}_{}{}^{+0.18}{}_{-0.12}{}^{}$	9.87	6.97${}_{}{}^{+0.20}{}_{-0.23}{}^{}$	-0.36${}_{}{}^{+0.11}{}_{-0.11}{}^{}$	0.91
38	UGC06877	178.55071	0.13681	18.30	8.94${}_{}{}^{+0.13}{}_{-0.09}{}^{}$	8.01	5.53${}_{}{}^{+0.10}{}_{-0.10}{}^{}$	-0.76${}_{}{}^{+0.06}{}_{-0.07}{}^{}$	0.13
39	UGC06879	178.60538	-2.3197	45.61	10.05${}_{}{}^{+0.11}{}_{-0.15}{}^{}$		7.29${}_{}{}^{+0.14}{}_{-0.12}{}^{}$	-0.48${}_{}{}^{+0.06}{}_{-0.09}{}^{}$
40	UGC06903	178.9025	1.23817	37.66	9.89${}_{}{}^{+0.09}{}_{-0.15}{}^{}$	9.68	7.17${}_{}{}^{+0.10}{}_{-0.09}{}^{}$	-0.24${}_{}{}^{+0.04}{}_{-0.04}{}^{}$	0.45
41	UGC06970	179.69101	-1.46169	30.31	9.39${}_{}{}^{+0.12}{}_{-0.15}{}^{}$	9.05	6.52${}_{}{}^{+0.35}{}_{-0.51}{}^{}$	-0.86${}_{}{}^{+0.10}{}_{-0.12}{}^{}$	0.38
42	UGC07000	180.295	-1.29751	30.76	9.11${}_{}{}^{+0.08}{}_{-0.16}{}^{}$	9.10	6.43${}_{}{}^{+0.12}{}_{-0.11}{}^{}$	-0.45${}_{}{}^{+0.04}{}_{-0.04}{}^{}$	0.56
43	UGC07053	181.0863	-1.53071	30.13	8.19${}_{}{}^{+0.18}{}_{-0.10}{}^{}$	9.25	4.80${}_{}{}^{+0.56}{}_{-0.54}{}^{}$	-1.03${}_{}{}^{+0.06}{}_{-0.07}{}^{}$	0.94
44	UGC07332	184.48653	0.43491	13.91	7.70${}_{}{}^{+0.14}{}_{-0.13}{}^{}$	8.95	4.31${}_{}{}^{+0.48}{}_{-0.40}{}^{}$	-1.39${}_{}{}^{+0.04}{}_{-0.04}{}^{}$	0.84
45	UGC07394	185.11652	1.46789	32.65	8.93${}_{}{}^{+0.14}{}_{-0.12}{}^{}$	9.24	6.87${}_{}{}^{+0.21}{}_{-0.23}{}^{}$	-1.22${}_{}{}^{+0.17}{}_{-0.34}{}^{}$	0.73
46	UGC07396	185.14135	0.78863	41.30	9.11${}_{}{}^{+0.14}{}_{-0.18}{}^{}$	9.11	6.49${}_{}{}^{+0.28}{}_{-0.24}{}^{}$	-0.77${}_{}{}^{+0.16}{}_{-0.25}{}^{}$	0.57
47	UGC07531	186.55054	-1.30325	39.44	8.60${}_{}{}^{+0.15}{}_{-0.08}{}^{}$	9.05	6.49${}_{}{}^{+0.39}{}_{-0.39}{}^{}$	-0.38${}_{}{}^{+0.04}{}_{-0.04}{}^{}$	0.79

Table 6. Table 5: Continued

#	Common name	RA	DEC	Distance	log $M_{{}_{\star }}$	log $M_{\text{H}\text{i}}$	log $M_d$	log SFR	$f_g$	HiGH	HAPLESS
		(J2000 deg)	(J2000 deg)	(Mpc)	($M_{\odot }$)	($M_{\odot }$)	($M_{\odot }$)	($M_{\odot }{\mathrm{yr}}^{-1}$)
48	UGC09215	215.86342	1.7243	25.65	9.31${}_{}{}^{+0.14}{}_{-0.04}{}^{}$	9.56	6.95${}_{}{}^{+0.09}{}_{-0.10}{}^{}$	-0.24${}_{}{}^{+0.02}{}_{-0.06}{}^{}$	0.70
49	UGC09299	217.39393	-0.01906	28.29	8.61${}_{}{}^{+0.19}{}_{-0.04}{}^{}$	9.94	6.39${}_{}{}^{+0.15}{}_{-0.14}{}^{}$	-0.55${}_{}{}^{+0.04}{}_{-0.04}{}^{}$	0.97
50	UGC09348	218.11926	0.29425	30.36	9.41${}_{}{}^{+0.14}{}_{-0.15}{}^{}$		6.66${}_{}{}^{+0.10}{}_{-0.10}{}^{}$	-0.89${}_{}{}^{+0.07}{}_{-0.10}{}^{}$
51	UGC09432	219.766	2.94708	28.53	8.19${}_{}{}^{+0.02}{}_{-0.14}{}^{}$	9.19	4.4${}_{}{}^{+0.48}{}_{-0.51}{}^{}$	-1.06${}_{}{}^{+0.05}{}_{-0.06}{}^{}$	0.93
52	UGC09470	220.45274	0.68756	34.03	8.90${}_{}{}^{+0.07}{}_{-0.13}{}^{}$	9.12	6.22${}_{}{}^{+0.17}{}_{-0.19}{}^{}$	-0.68${}_{}{}^{+0.04}{}_{-0.04}{}^{}$	0.69
53	UGC09482	220.69539	0.66151	32.39	8.72${}_{}{}^{+0.10}{}_{-0.14}{}^{}$	9.16	6.09${}_{}{}^{+0.31}{}_{-0.27}{}^{}$	-1.30${}_{}{}^{+0.14}{}_{-0.18}{}^{}$	0.79
54	UM452	176.75239	-0.29363	29.27	8.83${}_{}{}^{+0.15}{}_{-0.17}{}^{}$	8.24	5.34${}_{}{}^{+0.35}{}_{-0.34}{}^{}$	-1.37${}_{}{}^{+0.07}{}_{-0.17}{}^{}$	0.25
55	UM456	177.65105	-0.56613	33.73	8.28${}_{}{}^{+0.15}{}_{-0.15}{}^{}$	8.89	4.96${}_{}{}^{+0.59}{}_{-0.45}{}^{}$	-0.76${}_{}{}^{+0.04}{}_{-0.04}{}^{}$	0.84
56	UM456A	177.6415	-0.53795	35.53	7.88${}_{}{}^{+0.11}{}_{-0.13}{}^{}$	8.93	4.89${}_{}{}^{+0.65}{}_{-0.55}{}^{}$	-1.32${}_{}{}^{+0.12}{}_{-0.09}{}^{}$	0.94
57	UM491	184.97097	1.77326	39.71	8.46${}_{}{}^{+0.09}{}_{-0.10}{}^{}$	9.36	5.99${}_{}{}^{+0.34}{}_{-0.31}{}^{}$	-0.83${}_{}{}^{+0.05}{}_{-0.67}{}^{}$	0.91
58	UM501	186.59463	-1.2534	39.49	7.90${}_{}{}^{+0.10}{}_{-0.10}{}^{}$	9.39	5.1${}_{}{}^{+0.71}{}_{-0.54}{}^{}$	-1.06${}_{}{}^{+0.12}{}_{-0.04}{}^{}$	0.98

Table 7. Table 6: Metallicity measurements in the form 12 + log ​ ( O / H ) 12 log O H 12+{\rm log(O/H)} for the H i -selected H i GH sample ( DV17 ) and dust-selected HAPLESS sample ( C15 ). The check-marks indicate which sample the source is part of (24 sources are included in both samples).

#	Common name	12+log(O/H)				HiGH	HAPLESS
		O3N2	N2	PG16S	KE08/T04
1	2MASXJ14265308+0057462	${8.53}_{-0.12}^{+0.12}$	${8.53}_{-0.12}^{+0.12}$	${8.37}_{-0.12}^{+0.12}$	${8.77}_{-0.13}^{+0.13}$
2	CGCG014-010
3	CGCG014-014	${8.25}_{-0.14}^{+0.13}$	${8.15}_{-0.13}^{+0.12}$	${7.75}_{-0.26}^{+0.37}$	${8.42}_{-0.14}^{+0.13}$
4	CGCG019-003	${8.21}_{-0.12}^{+0.12}$	${8.22}_{-0.12}^{+0.12}$	${8.14}_{-0.13}^{+0.12}$	${8.36}_{-0.12}^{+0.12}$
5	CGCG019-084	${8.59}_{-0.13}^{+0.13}$	${8.50}_{-0.12}^{+0.12}$		${8.84}_{-0.13}^{+0.13}$
6	FGC1412
7	IC1010
8	IC1011	${8.73}_{-0.12}^{+0.12}$	${8.77}_{-0.20}^{+0.16}$	${8.60}_{-0.12}^{+0.12}$	${9.01}_{-0.13}^{+0.13}$
9	LEDA1241857	${8.34}_{-0.12}^{+0.12}$	${8.29}_{-0.12}^{+0.12}$	${8.19}_{-0.12}^{+0.12}$	${8.54}_{-0.12}^{+0.12}$
10	MGC0066574
11	MGC0068525
12	NGC4030	${8.81}_{-0.11}^{+0.11}$	${8.55}_{-0.11}^{+0.11}$	${8.54}_{-0.11}^{+0.11}$	${9.11}_{-0.11}^{+0.11}$
13	NGC4030b	${8.36}_{-0.12}^{+0.12}$	${8.32}_{-0.12}^{+0.12}$	${8.30}_{-0.12}^{+0.12}$	${8.57}_{-0.13}^{+0.13}$
14	NGC4202	${8.71}_{-0.13}^{+0.16}$	${8.44}_{-0.12}^{+0.12}$	${8.47}_{-0.12}^{+0.12}$	${8.98}_{-0.15}^{+0.17}$
15	NGC5496	${8.34}_{-0.10}^{+0.10}$	${8.29}_{-0.10}^{+0.10}$	${8.19}_{-0.10}^{+0.10}$	${8.54}_{-0.10}^{+0.10}$
16	NGC5584	${8.49}_{-0.10}^{+0.10}$	${8.43}_{-0.10}^{+0.10}$	${8.36}_{-0.10}^{+0.10}$	${8.72}_{-0.10}^{+0.10}$
17	NGC5690	${8.76}_{-0.11}^{+0.11}$	${8.58}_{-0.11}^{+0.11}$	${8.48}_{-0.11}^{+0.11}$	${9.04}_{-0.11}^{+0.11}$
18	NGC5691	${8.57}_{-0.10}^{+0.10}$	${8.54}_{-0.10}^{+0.10}$	${8.42}_{-0.10}^{+0.10}$	${8.82}_{-0.10}^{+0.10}$
19	NGC5705	${8.56}_{-0.11}^{+0.11}$	${8.48}_{-0.11}^{+0.11}$	${8.30}_{-0.11}^{+0.11}$	${8.81}_{-0.12}^{+0.12}$
20	NGC5713	${8.76}_{-0.10}^{+0.10}$	${8.63}_{-0.10}^{+0.10}$	${8.53}_{-0.10}^{+0.10}$	${9.05}_{-0.10}^{+0.10}$
21	NGC5719
22	NGC5725	${8.42}_{-0.10}^{+0.10}$	${8.41}_{-0.10}^{+0.10}$	${8.31}_{-0.10}^{+0.10}$	${8.65}_{-0.10}^{+0.10}$
23	NGC5733	${8.28}_{-0.11}^{+0.10}$	${8.28}_{-0.11}^{+0.11}$	${8.18}_{-0.11}^{+0.11}$	${8.48}_{-0.11}^{+0.11}$
24	NGC5738
25	NGC5740	${8.68}_{-0.11}^{+0.11}$	${8.70}_{-0.11}^{+0.11}$	${8.57}_{-0.11}^{+0.11}$	${8.95}_{-0.11}^{+0.11}$

Table 8. Table 6: Continued

#	Common name	12+log(O/H)				HiGH	HAPLESS
		O3N2	N2	PG16S	KE08/T04
26	NGC5746
27	NGC5750	${8.75}_{-0.12}^{+0.12}$	${8.79}_{-0.12}^{+0.12}$	${8.59}_{-0.12}^{+0.12}$	${9.03}_{-0.12}^{+0.12}$
28	PGC037392	${8.33}_{-0.12}^{+0.12}$	${8.36}_{-0.12}^{+0.12}$	${8.19}_{-0.12}^{+0.12}$	${8.54}_{-0.12}^{+0.12}$
29	PGC051719	${8.38}_{-0.11}^{+0.11}$	${8.33}_{-0.11}^{+0.11}$	${8.24}_{-0.11}^{+0.11}$	${8.59}_{-0.11}^{+0.11}$
30	PGC052652	${8.35}_{-0.12}^{+0.12}$	${8.33}_{-0.12}^{+0.12}$	${8.27}_{-0.12}^{+0.12}$	${8.56}_{-0.12}^{+0.12}$
31	SDSSJ084258.35+003838.5	${8.63}_{-0.12}^{+0.12}$	${8.58}_{-0.12}^{+0.12}$	${8.47}_{-0.12}^{+0.12}$	${8.89}_{-0.13}^{+0.13}$
32	SDSSJ143353.30+012905.6
33	UGC04673	${8.50}_{-0.11}^{+0.11}$	${8.45}_{-0.11}^{+0.11}$	${8.36}_{-0.11}^{+0.11}$	${8.75}_{-0.12}^{+0.12}$
34	UGC04684	${8.58}_{-0.12}^{+0.12}$	${8.48}_{-0.12}^{+0.12}$		${8.84}_{-0.13}^{+0.13}$
35	UGC04996	${8.45}_{-0.11}^{+0.11}$	${8.36}_{-0.11}^{+0.11}$	${8.18}_{-0.11}^{+0.11}$	${8.68}_{-0.12}^{+0.12}$
36	UGC06578	${8.04}_{-0.11}^{+0.11}$	${7.99}_{-0.11}^{+0.11}$	${7.87}_{-0.12}^{+0.12}$	${8.25}_{-0.13}^{+0.13}$
37	UGC06780	${8.42}_{-0.11}^{+0.11}$	${8.32}_{-0.11}^{+0.11}$	${8.06}_{-0.11}^{+0.11}$	${8.64}_{-0.12}^{+0.12}$
38	UGC06877	${8.58}_{-0.12}^{+0.12}$	${8.53}_{-0.12}^{+0.12}$	${8.52}_{-0.12}^{+0.12}$	${8.83}_{-0.12}^{+0.12}$
39	UGC06879
40	UGC06903	${8.59}_{-0.11}^{+0.12}$	${8.49}_{-0.12}^{+0.12}$	${8.33}_{-0.12}^{+0.12}$	${8.85}_{-0.12}^{+0.12}$
41	UGC06970	${8.54}_{-0.13}^{+0.13}$	${8.45}_{-0.16}^{+0.16}$	${8.16}_{-0.19}^{+0.16}$	${8.79}_{-0.15}^{+0.15}$
42	UGC07000	${8.39}_{-0.10}^{+0.10}$	${8.34}_{-0.10}^{+0.10}$	${8.25}_{-0.10}^{+0.10}$	${8.59}_{-0.11}^{+0.11}$
43	UGC07053	${8.21}_{-0.11}^{+0.11}$	${8.15}_{-0.11}^{+0.11}$	${7.88}_{-0.12}^{+0.12}$	${8.36}_{-0.12}^{+0.12}$
44	UGC07332
45	UGC07394	${8.28}_{-0.11}^{+0.11}$	${8.20}_{-0.11}^{+0.11}$	${7.94}_{-0.13}^{+0.12}$	${8.47}_{-0.12}^{+0.12}$
46	UGC07396	${8.44}_{-0.12}^{+0.12}$	${8.39}_{-0.12}^{+0.12}$	${8.26}_{-0.12}^{+0.12}$	${8.68}_{-0.12}^{+0.12}$
47	UGC07531
48	UGC09215	${8.40}_{-0.10}^{+0.10}$	${8.37}_{-0.10}^{+0.10}$	${8.29}_{-0.10}^{+0.10}$	${8.62}_{-0.11}^{+0.11}$
49	UGC09299	${8.39}_{-0.11}^{+0.11}$	${8.36}_{-0.11}^{+0.11}$	${8.25}_{-0.11}^{+0.11}$	${8.60}_{-0.12}^{+0.12}$
50	UGC09348	${8.49}_{-0.12}^{+0.12}$	${8.49}_{-0.12}^{+0.12}$	${8.33}_{-0.12}^{+0.12}$	${8.73}_{-0.12}^{+0.12}$
51	UGC09432	${8.25}_{-0.12}^{+0.12}$	${8.21}_{-0.12}^{+0.12}$	${8.06}_{-0.16}^{+0.12}$	${8.42}_{-0.13}^{+0.13}$
52	UGC09470	${8.13}_{-0.12}^{+0.12}$	${8.15}_{-0.12}^{+0.12}$	${7.94}_{-0.12}^{+0.12}$	${8.22}_{-0.13}^{+0.13}$
53	UGC09482	${8.33}_{-0.11}^{+0.11}$	${8.27}_{-0.11}^{+0.11}$	${8.14}_{-0.11}^{+0.11}$	${8.54}_{-0.12}^{+0.12}$
54	UM452	${8.27}_{-0.12}^{+0.12}$	${8.26}_{-0.12}^{+0.12}$	${8.19}_{-0.12}^{+0.12}$	${8.45}_{-0.12}^{+0.12}$
55	UM456	${8.04}_{-0.11}^{+0.11}$	${8.05}_{-0.11}^{+0.11}$	${7.81}_{-0.11}^{+0.11}$	${8.20}_{-0.13}^{+0.13}$
56	UM456A	${8.06}_{-0.11}^{+0.11}$	${8.04}_{-0.11}^{+0.11}$	${7.72}_{-0.11}^{+0.11}$	${8.10}_{-0.13}^{+0.13}$
57	UM491	${8.25}_{-0.12}^{+0.12}$	${8.24}_{-0.12}^{+0.12}$	${8.18}_{-0.12}^{+0.12}$	${8.42}_{-0.12}^{+0.12}$
58	UM501

Table 9. Table 7: Emission line measurements for the H i GH and HAPLESS samples. The first three columns give the ID from Table 5 , the common name, and the GAMA cataID respectively. All emission lines have been corrected for reddening using the Balmer decrement C ​ ( H ​ β ) 𝐶 𝐻 𝛽 C(H\beta) and the Cardelli et al. ( 1989 ) dust obscuration curve. The Origin column specifies whether the stellar absorption corrected fluxes were extracted using GANDALF or the GaussFitComplexv05 (GFC) catalogue. Measurements from multiple regions of the galaxies are presented where appropriate.

ID	name	cataID	$C\left(H\beta \right)$									Origin	HiGH	HAPLESS
				$I_{\lambda }/I_{H\beta }$
				[Oii]	$H\beta$	[Oiii,4959]	[Oiii,5007]	$H\alpha$	[Nii]	[Sii,6713]	[Sii,6731]
1	2MASXJ142…	106616	0.53	$2.36\pm 0.10$	$1.00\pm 0.02$	$0.37\pm 0.01$	$1.04\pm 0.02$	$2.86\pm 0.03$	$0.703\pm 0.010$	$0.674\pm 0.013$	$0.539\pm 0.011$	GANDALF
3	CGCG014-014	86115	0.69	$5.78\pm 0.53$	$1.00\pm 0.31$	$0.59\pm 0.05$	$1.50\pm 0.09$	$2.86\pm 0.09$	$0.139\pm 0.043$	$0.357\pm 0.035$	$0.237\pm 0.029$	GFC
4	CGCG019-003	227753	0.0	$3.32\pm 0.11$	$1.00\pm 0.01$	$1.05\pm 0.01$	$3.00\pm 0.02$	$2.60\pm 0.02$	$0.190\pm 0.008$	$0.309\pm 0.009$	$0.206\pm 0.008$	GANDALF
5	CGCG019-084	240108	1.2		$1.00\pm 0.22$	$0.23\pm 0.07$	$0.63\pm 0.19$	$2.86\pm 0.10$	$0.658\pm 0.066$	$0.884\pm 0.066$		GANDALF
6	FGC1412	611445	0.24		$1.00\pm 1.27$	$0.17\pm 2.52$	$0.49\pm 7.17$	$2.86\pm 0.40$	$0.224\pm 1.491$	$0.154\pm 0.794$		GANDALF
6	FGC1412	611446	0.18	$2.61\pm 20.76$	$1.00\pm 1.32$	$0.37\pm 0.63$	$1.05\pm 1.80$	$2.86\pm 0.91$	$0.476\pm 0.676$	$0.927\pm 0.587$	$0.620\pm 0.757$	GANDALF
8	IC1011	106717	0.92		$1.00\pm 0.05$	$0.14\pm 0.02$	$0.41\pm 0.03$	$2.87\pm 0.68$	$1.211\pm 0.028$	$0.458\pm 0.019$	$0.359\pm 0.017$	GFC
9	LEDA1241857	367540	0.1		$1.00\pm 0.14$	$0.63\pm 0.03$	$1.78\pm 0.05$	$2.86\pm 0.06$	$0.305\pm 0.022$	$0.452\pm 0.025$	$0.325\pm 0.016$	GFC
10	MGC0066574	594420	0.0	$3.79\pm 4.23$	$1.00\pm 0.55$	$0.20\pm 0.65$	$0.58\pm 1.87$	$0.77\pm 0.28$	$0.226\pm 0.382$	$0.257\pm 0.296$	$0.399\pm 0.331$	GANDALF
12	NGC4030	31521	0.0	$0.35\pm 0.15$	$1.00\pm 0.03$	$0.05\pm 0.01$	$0.13\pm 0.03$	$2.09\pm 0.02$	$0.554\pm 0.020$	$0.149\pm 0.017$	$0.108\pm 0.016$	GANDALF
12	NGC4030	31523	0.55	$2.55\pm 0.22$	$1.00\pm 0.03$	$0.03\pm 0.01$	$0.09\pm 0.02$	$2.86\pm 0.05$	$0.630\pm 0.015$	$0.281\pm 0.012$	$0.240\pm 0.009$	GANDALF
12	NGC4030	690077	0.0	$14.76\pm 2.09$	$1.00\pm 0.05$	$0.08\pm 0.02$	$0.24\pm 0.04$	$2.31\pm 0.04$	$0.689\pm 0.031$	$0.230\pm 0.036$	$0.159\pm 0.036$	GANDALF
13	NGC4030b	584731	0.0	$3.94\pm 0.37$	$1.00\pm 0.04$	$0.60\pm 0.01$	$1.73\pm 0.04$	$2.53\pm 0.03$	$0.298\pm 0.017$	$0.265\pm 0.015$	$0.168\pm 0.015$	GANDALF
14	NGC4202	32362	2.57	$4.25\pm 25.06$	$1.00\pm 0.18$	$0.44\pm 0.16$	$0.21\pm 0.12$	$2.88\pm 0.08$	$0.538\pm 0.027$	$0.256\pm 0.019$	$0.164\pm 0.015$	GFC
15	NGC5496	496980	0.0	$2.20\pm 5.24$	$1.00\pm 0.42$	$0.33\pm 0.03$	$0.89\pm 0.05$	$1.65\pm 0.05$	$0.170\pm 0.031$	$0.378\pm 0.028$	$0.262\pm 0.022$	GFC
15	NGC5496	463393	0.25	$2.35\pm 0.30$	$1.00\pm 0.03$	$0.48\pm 0.01$	$1.36\pm 0.03$	$2.86\pm 0.02$	$0.342\pm 0.015$	$0.513\pm 0.016$	$0.367\pm 0.015$	GANDALF
15	NGC5496	463394	1.25	$2.46\pm 0.25$	$1.00\pm 0.04$	$0.83\pm 0.03$	$2.30\pm 0.08$	$2.86\pm 0.07$	$0.272\pm 0.009$	$0.265\pm 0.009$	$0.185\pm 0.007$	GANDALF
15	NGC5496	496979	0.91	$5.29\pm 0.86$	$1.00\pm 0.13$	$0.74\pm 0.08$	$2.05\pm 0.23$	$2.86\pm 0.24$	$0.234\pm 0.022$	$0.289\pm 0.024$	$0.209\pm 0.018$	GANDALF
15	NGC5496	496981	1.21	$10.32\pm 0.71$	$1.00\pm 0.03$	$0.81\pm 0.01$	$2.24\pm 0.02$	$2.86\pm 0.02$	$0.344\pm 0.008$	$0.423\pm 0.008$	$0.295\pm 0.008$	GANDALF
15	NGC5496	496982	0.0	$6.06\pm 0.59$	$1.00\pm 0.04$	$0.89\pm 0.01$	$2.54\pm 0.04$	$2.05\pm 0.02$	$0.147\pm 0.013$	$0.273\pm 0.012$	$0.199\pm 0.013$	GANDALF
15	NGC5496	496985	0.35	$4.85\pm 0.72$	$1.00\pm 0.06$	$0.43\pm 0.02$	$1.22\pm 0.05$	$2.86\pm 0.06$	$0.454\pm 0.032$	$0.664\pm 0.031$	$0.462\pm 0.034$	GANDALF
15	NGC5496	496986	0.0	$11.33\pm 1.72$	$1.00\pm 0.08$	$0.46\pm 0.02$	$1.32\pm 0.06$	$2.57\pm 0.06$	$0.238\pm 0.044$	$0.436\pm 0.041$	$0.299\pm 0.045$	GANDALF
16	NGC5584	693091	0.29	$1.38\pm 7.39$	$1.00\pm 0.05$	$0.43\pm 0.02$	$1.26\pm 0.04$	$2.86\pm 0.04$	$0.448\pm 0.018$	$0.532\pm 0.031$	$0.383\pm 0.023$	GFC
16	NGC5584	63349	0.0	$187.48\pm 0.84$	$1.00\pm 0.12$	$0.18\pm 0.04$	$0.51\pm 0.12$	$2.56\pm 0.10$	$0.365\pm 0.089$	$0.149\pm 0.124$	$0.179\pm 0.116$	GANDALF
16	NGC5584	63351	0.0	$1.91\pm 0.27$	$1.00\pm 0.09$	$0.35\pm 0.02$	$1.00\pm 0.07$	$2.86\pm 0.17$	$0.509\pm 0.028$	$0.448\pm 0.030$	$0.320\pm 0.022$	GANDALF
16	NGC5584	63353	0.86	$2.54\pm 0.32$	$1.00\pm 0.04$	$0.33\pm 0.01$	$0.92\pm 0.04$	$2.86\pm 0.05$	$0.577\pm 0.014$	$0.428\pm 0.012$	$0.300\pm 0.013$	GANDALF
16	NGC5584	63354	0.66	$2.84\pm 0.70$	$1.00\pm 0.14$	$0.31\pm 0.04$	$0.89\pm 0.12$	$2.86\pm 0.23$	$0.535\pm 0.054$	$0.569\pm 0.059$	$0.397\pm 0.042$	GANDALF
16	NGC5584	693086	0.01	$3.23\pm 0.77$	$1.00\pm 0.18$	$1.14\pm 0.20$	$3.25\pm 0.56$	$2.86\pm 0.43$	$0.239\pm 0.032$	$0.173\pm 0.023$	$0.138\pm 0.018$	GANDALF
16	NGC5584	693088	0.27	$2.16\pm 0.22$	$1.00\pm 0.02$	$0.25\pm 0.01$	$0.72\pm 0.02$	$2.86\pm 0.02$	$0.882\pm 0.013$	$0.555\pm 0.011$	$0.445\pm 0.010$	GANDALF
17	NGC5690	262444	1.63	$4.54\pm 25.79$	$1.00\pm 0.07$	$0.08\pm 0.03$	$0.22\pm 0.05$	$2.87\pm 0.05$	$0.984\pm 0.029$	$0.499\pm 0.037$	$0.352\pm 0.031$	GFC
17	NGC5690	262445	2.13	$11.63\pm 25.93$	$1.00\pm 0.05$	$0.05\pm 0.02$	$0.16\pm 0.04$	$2.87\pm 0.03$	$0.625\pm 0.010$	$0.356\pm 0.008$	$0.251\pm 0.007$	GFC
17	NGC5690	716324	0.73	$2.00\pm 0.88$	$1.00\pm 0.07$	$0.10\pm 0.02$	$0.28\pm 0.04$	$2.86\pm 0.07$	$0.805\pm 0.032$	$0.542\pm 0.027$	$0.340\pm 0.023$	GANDALF
18	NGC5691	64554	0.4	$3.71\pm 0.34$	$1.00\pm 0.03$	$0.25\pm 0.01$	$0.70\pm 0.03$	$2.86\pm 0.02$	$0.773\pm 0.015$	$0.698\pm 0.015$	$0.488\pm 0.015$	GANDALF
18	NGC5691	693423	0.0	$6.78\pm 0.25$	$1.00\pm 0.01$	$0.39\pm 0.00$	$1.11\pm 0.01$	$2.75\pm 0.01$	$0.804\pm 0.005$	$0.529\pm 0.005$	$0.442\pm 0.006$	GANDALF
18	NGC5691	693424	0.0	$1.67\pm 0.10$	$1.00\pm 0.02$	$0.23\pm 0.01$	$0.64\pm 0.01$	$2.50\pm 0.02$	$0.688\pm 0.011$	$0.510\pm 0.011$	$0.365\pm 0.010$	GANDALF
18	NGC5691	693425	0.75	$1.90\pm 0.12$	$1.00\pm 0.01$	$0.33\pm 0.00$	$0.92\pm 0.01$	$2.86\pm 0.01$	$0.888\pm 0.006$	$0.669\pm 0.008$	$0.527\pm 0.007$	GANDALF
18	NGC5691	693426	0.62	$3.13\pm 0.16$	$1.00\pm 0.02$	$0.29\pm 0.00$	$0.81\pm 0.01$	$2.86\pm 0.01$	$0.611\pm 0.007$	$0.397\pm 0.007$	$0.283\pm 0.007$	GANDALF
18	NGC5691	756783	0.58		$1.00\pm 0.02$	$0.24\pm 0.00$	$0.66\pm 0.01$	$2.86\pm 0.03$	$0.491\pm 0.008$	$0.370\pm 0.008$	$0.248\pm 0.007$	GANDALF
19	NGC5705	49167	0.0	$2.18\pm 0.46$	$1.00\pm 0.07$	$0.25\pm 0.02$	$0.71\pm 0.07$	$2.29\pm 0.09$	$0.590\pm 0.054$	$0.772\pm 0.059$	$0.532\pm 0.050$	GANDALF
19	NGC5705	49169	0.21	$4.11\pm 0.19$	$1.00\pm 0.03$	$0.28\pm 0.01$	$0.81\pm 0.02$	$2.86\pm 0.07$	$0.530\pm 0.018$	$0.567\pm 0.018$	$0.431\pm 0.015$	GANDALF
19	NGC5705	49171	0.0	$4.53\pm 1.82$	$1.00\pm 0.33$	$0.20\pm 0.05$	$0.57\pm 0.15$	$0.98\pm 0.14$	$0.233\pm 0.134$	$0.336\pm 0.103$	$0.211\pm 0.148$	GANDALF

Table 10. Table 7: Continued

ID	name	cataID	$C\left(H\beta \right)$									Origin	HiGH	HAPLESS
				$I_{\lambda }/I_{H\beta }$
				[Oii]	$H\beta$	[Oiii,4959]	[Oiii,5007]	$H\alpha$	[Nii]	[Sii,6713]	[Sii,6731]
20	NGC5713	693034	1.38	$1.11\pm 9.33$	$1.00\pm 0.02$	$0.06\pm 0.01$	$0.19\pm 0.01$	$2.87\pm 0.01$	$1.001\pm 0.007$	$0.443\pm 0.005$	$0.328\pm 0.004$	GFC
20	NGC5713	64772	1.52	$7.49\pm 0.43$	$1.00\pm 0.02$	$0.16\pm 0.00$	$0.45\pm 0.01$	$2.86\pm 0.04$	$0.855\pm 0.012$	$0.386\pm 0.006$	$0.279\pm 0.006$	GANDALF
20	NGC5713	64773	0.53	$1.18\pm 0.06$	$1.00\pm 0.01$	$0.10\pm 0.00$	$0.29\pm 0.01$	$2.86\pm 0.01$	$0.912\pm 0.005$	$0.454\pm 0.005$	$0.334\pm 0.004$	GANDALF
20	NGC5713	693033	0.51	$2.10\pm 0.11$	$1.00\pm 0.01$	$0.11\pm 0.00$	$0.30\pm 0.01$	$2.86\pm 0.01$	$0.871\pm 0.005$	$0.387\pm 0.004$	$0.285\pm 0.004$	GANDALF
20	NGC5713	693035	0.94		$1.00\pm 0.01$	$0.09\pm 0.00$	$0.24\pm 0.00$	$2.86\pm 0.02$	$0.779\pm 0.006$	$0.326\pm 0.004$	$0.227\pm 0.004$	GANDALF
20	NGC5713	693036	0.19	$1.24\pm 0.07$	$1.00\pm 0.01$	$0.06\pm 0.00$	$0.16\pm 0.01$	$2.86\pm 0.01$	$1.001\pm 0.008$	$0.483\pm 0.007$	$0.348\pm 0.006$	GANDALF
20	NGC5713	693037	0.62	$1.29\pm 0.16$	$1.00\pm 0.02$	$0.07\pm 0.01$	$0.21\pm 0.02$	$2.86\pm 0.02$	$0.895\pm 0.013$	$0.452\pm 0.011$	$0.330\pm 0.010$	GANDALF
20	NGC5713	693038	0.63	$0.76\pm 0.29$	$1.00\pm 0.04$	$0.09\pm 0.01$	$0.24\pm 0.03$	$2.86\pm 0.02$	$1.005\pm 0.017$	$0.501\pm 0.016$	$0.359\pm 0.015$	GANDALF
22	NGC5725	343415	0.24	$5.46\pm 25.00$	$1.00\pm 0.28$	$0.31\pm 0.13$	$0.92\pm 0.21$	$2.86\pm 0.33$	$0.550\pm 0.151$	$0.942\pm 0.161$	$0.622\pm 0.123$	GFC
22	NGC5725	343407	1.02	$9.35\pm 69.01$	$1.00\pm 0.04$	$0.57\pm 0.02$	$1.75\pm 0.04$	$2.87\pm 0.02$	$0.435\pm 0.012$	$0.482\pm 0.011$	$0.339\pm 0.009$	GFC
22	NGC5725	343410	0.34	$3.74\pm 15.39$	$1.00\pm 0.14$	$0.34\pm 0.02$	$0.96\pm 0.04$	$2.86\pm 0.05$	$0.461\pm 0.023$	$0.539\pm 0.021$	$0.354\pm 0.016$	GFC
22	NGC5725	343414	0.69	$3.97\pm 11.92$	$1.00\pm 0.10$	$0.44\pm 0.05$	$1.32\pm 0.09$	$2.86\pm 0.06$	$0.491\pm 0.037$	$0.766\pm 0.070$	$0.481\pm 0.065$	GFC
22	NGC5725	343405	0.0		$1.00\pm 0.01$	$0.55\pm 0.01$	$1.59\pm 0.02$	$2.72\pm 0.04$	$0.471\pm 0.008$	$0.443\pm 0.009$	$0.333\pm 0.006$	GANDALF
22	NGC5725	343409	0.31	$1.34\pm 0.29$	$1.00\pm 0.05$	$0.68\pm 0.02$	$1.94\pm 0.06$	$2.86\pm 0.04$	$0.369\pm 0.022$	$0.418\pm 0.023$	$0.306\pm 0.022$	GANDALF
22	NGC5725	343411	0.0	$2.80\pm 0.27$	$1.00\pm 0.03$	$0.54\pm 0.01$	$1.53\pm 0.03$	$2.63\pm 0.02$	$0.453\pm 0.016$	$0.419\pm 0.018$	$0.306\pm 0.017$	GANDALF
22	NGC5725	343413	0.15	$1.22\pm 0.50$	$1.00\pm 0.14$	$0.80\pm 0.10$	$2.27\pm 0.28$	$2.86\pm 0.28$	$0.265\pm 0.038$	$0.232\pm 0.036$	$0.139\pm 0.028$	GANDALF
22	NGC5725	722438	0.0	$5.81\pm 0.42$	$1.00\pm 0.04$	$0.47\pm 0.02$	$1.35\pm 0.05$	$2.66\pm 0.05$	$0.824\pm 0.033$	$0.721\pm 0.032$	$0.635\pm 0.039$	GANDALF
23	NGC5733	64893	0.43		$1.00\pm 0.04$	$0.84\pm 0.02$	$2.60\pm 0.05$	$2.86\pm 0.05$	$0.361\pm 0.020$	$0.542\pm 0.019$	$0.378\pm 0.015$	GFC
23	NGC5733	64894	0.0	$2.85\pm 0.12$	$1.00\pm 0.02$	$0.81\pm 0.01$	$2.30\pm 0.02$	$2.69\pm 0.02$	$0.237\pm 0.007$	$0.329\pm 0.007$	$0.232\pm 0.007$	GANDALF
23	NGC5733	64895	0.0	$0.98\pm 0.11$	$1.00\pm 0.03$	$0.85\pm 0.01$	$2.42\pm 0.04$	$2.66\pm 0.02$	$0.225\pm 0.014$	$0.412\pm 0.015$	$0.278\pm 0.012$	GANDALF
25	NGC5740	321075	1.87		$1.00\pm 0.04$	$0.20\pm 0.01$	$0.58\pm 0.03$	$2.87\pm 0.04$	$1.116\pm 0.019$	$0.324\pm 0.013$	$0.268\pm 0.013$	GFC
25	NGC5740	321076	0.11	$2.46\pm 0.57$	$1.00\pm 0.06$	$0.10\pm 0.02$	$0.30\pm 0.05$	$2.86\pm 0.08$	$1.078\pm 0.039$	$0.452\pm 0.028$	$0.318\pm 0.027$	GANDALF
25	NGC5740	321077	0.0	$2.64\pm 0.29$	$1.00\pm 0.03$	$0.26\pm 0.01$	$0.74\pm 0.02$	$2.72\pm 0.02$	$0.930\pm 0.015$	$0.415\pm 0.013$	$0.296\pm 0.015$	GANDALF
27	NGC5750	65076	0.0	$2.42\pm 0.20$	$1.00\pm 0.02$	$0.13\pm 0.01$	$0.38\pm 0.02$	$2.68\pm 0.02$	$1.177\pm 0.012$	$0.456\pm 0.012$	$0.336\pm 0.011$	GANDALF
28	PGC037392	288461	0.06		$1.00\pm 0.05$	$0.85\pm 0.02$	$2.44\pm 0.06$	$2.86\pm 0.06$	$0.406\pm 0.046$	$0.815\pm 0.046$	$0.492\pm 0.046$	GANDALF
29	PGC051719	92677	0.32	$2.66\pm 0.20$	$1.00\pm 0.05$	$0.85\pm 0.04$	$2.41\pm 0.13$	$2.86\pm 0.11$	$0.244\pm 0.011$	$0.331\pm 0.014$	$0.236\pm 0.011$	GANDALF
29	PGC051719	92676	0.22	$4.08\pm 0.32$	$1.00\pm 0.04$	$0.35\pm 0.02$	$1.00\pm 0.04$	$2.86\pm 0.06$	$0.511\pm 0.022$	$0.656\pm 0.025$	$0.427\pm 0.024$	GANDALF
30	PGC052652	240202	0.46	$3.20\pm 0.25$	$1.00\pm 0.02$	$0.65\pm 0.01$	$1.83\pm 0.02$	$2.86\pm 0.01$	$0.352\pm 0.006$	$0.389\pm 0.007$	$0.274\pm 0.006$	GANDALF
31	SDSSJ084…	622084	0.19	$1.75\pm 0.09$	$1.00\pm 0.02$	$0.20\pm 0.01$	$0.56\pm 0.02$	$2.86\pm 0.02$	$0.796\pm 0.011$	$0.480\pm 0.013$	$0.356\pm 0.011$	GANDALF
33	UGC04673	517868	0.65		$1.00\pm 0.17$	$0.21\pm 0.07$	$0.63\pm 0.12$	$2.86\pm 0.24$	$0.776\pm 0.060$	$0.672\pm 0.071$	$0.568\pm 0.056$	GFC
33	UGC04673	517869	0.53	$2.89\pm 0.33$	$1.00\pm 0.07$	$0.51\pm 0.03$	$1.42\pm 0.09$	$2.86\pm 0.12$	$0.426\pm 0.025$	$0.372\pm 0.021$	$0.257\pm 0.017$	GANDALF
34	UGC04684	600168	0.31		$1.00\pm 0.05$	$0.22\pm 0.01$	$0.62\pm 0.04$	$2.86\pm 0.05$	$0.615\pm 0.027$	$0.629\pm 0.025$		GANDALF
35	UGC04996	198771	0.0	$1.66\pm 0.20$	$1.00\pm 0.07$	$0.27\pm 0.02$	$0.78\pm 0.06$	$1.91\pm 0.05$	$0.346\pm 0.033$	$0.531\pm 0.040$	$0.396\pm 0.036$	GANDALF
35	UGC04996	198772	0.22	$6.01\pm 0.89$	$1.00\pm 0.07$	$0.51\pm 0.02$	$1.44\pm 0.06$	$2.86\pm 0.05$	$0.301\pm 0.037$	$0.466\pm 0.035$	$0.405\pm 0.032$	GANDALF
36	UGC06578	6821	0.0	$0.62\pm 14.85$	$1.00\pm 0.21$	$1.39\pm 0.00$	$4.13\pm 0.01$	$1.19\pm 0.00$	$0.013\pm 0.000$	$0.031\pm 0.000$	$0.023\pm 0.000$	GFC
36	UGC06578	6822	0.25		$1.00\pm 0.02$	$1.01\pm 0.01$	$2.88\pm 0.03$	$2.86\pm 0.03$	$0.126\pm 0.005$	$0.292\pm 0.005$	$0.200\pm 0.007$	GANDALF
37	UGC06780	177588	0.13	$1.76\pm 0.39$	$1.00\pm 0.05$	$0.33\pm 0.02$	$0.95\pm 0.05$	$2.86\pm 0.06$	$0.500\pm 0.043$	$0.640\pm 0.044$	$0.459\pm 0.045$	GANDALF
37	UGC06780	177591	0.0	$3.09\pm 0.28$	$1.00\pm 0.05$	$0.43\pm 0.01$	$1.24\pm 0.04$	$2.28\pm 0.03$	$0.169\pm 0.016$	$0.317\pm 0.021$	$0.207\pm 0.018$	GANDALF
38	UGC06877	70114	1.02	$2.48\pm 0.06$	$1.00\pm 0.02$	$0.24\pm 0.01$	$0.72\pm 0.01$	$2.87\pm 0.02$	$0.712\pm 0.005$	$0.251\pm 0.007$	$0.193\pm 0.003$	GFC
40	UGC06903	22742	0.0	$4.11\pm 3.47$	$1.00\pm 0.25$	$0.17\pm 0.08$	$0.49\pm 0.24$	$1.93\pm 0.29$	$0.524\pm 0.126$	$0.445\pm 0.091$	$0.323\pm 0.134$	GANDALF
40	UGC06903	272331	0.33	$2.04\pm 0.44$	$1.00\pm 0.06$	$0.23\pm 0.02$	$0.65\pm 0.07$	$2.86\pm 0.07$	$0.592\pm 0.043$	$0.678\pm 0.050$	$0.429\pm 0.043$	GANDALF
41	UGC06970	185266	0.0	$1.42\pm 0.90$	$1.00\pm 0.19$	$0.27\pm 0.07$	$0.76\pm 0.21$	$1.88\pm 0.12$	$0.370\pm 0.125$	$0.687\pm 0.121$	$0.463\pm 0.127$	GANDALF

Table 11. Table 7: Continued

ID	name	cataID	$C\left(H\beta \right)$									Origin	HiGH	HAPLESS
				$I_{\lambda }/I_{H\beta }$
				[Oii]	$H\beta$	[Oiii,4959]	[Oiii,5007]	$H\alpha$	[Nii]	[Sii,6713]	[Sii,6731]
42	UGC07000	144491	0.56	$3.52\pm 13.57$	$1.00\pm 0.15$	$0.44\pm 0.06$	$1.64\pm 0.15$	$2.86\pm 0.08$	$0.450\pm 0.051$	$0.442\pm 0.041$	$0.362\pm 0.036$	GFC
42	UGC07000	144493	0.0	$2.97\pm 0.14$	$1.00\pm 0.02$	$0.74\pm 0.01$	$2.11\pm 0.02$	$2.11\pm 0.02$	$0.238\pm 0.010$	$0.301\pm 0.011$	$0.206\pm 0.010$	GANDALF
42	UGC07000	144494	0.23	$3.24\pm 0.37$	$1.00\pm 0.04$	$0.32\pm 0.01$	$0.91\pm 0.03$	$2.86\pm 0.04$	$0.453\pm 0.023$	$0.544\pm 0.023$	$0.395\pm 0.025$	GANDALF
42	UGC07000	144495	0.0	$5.38\pm 1.69$	$1.00\pm 0.20$	$0.62\pm 0.12$	$1.77\pm 0.34$	$2.69\pm 0.34$	$0.332\pm 0.057$	$0.430\pm 0.062$	$0.286\pm 0.054$	GANDALF
42	UGC07000	700775	0.27	$1.02\pm 0.50$	$1.00\pm 0.36$	$1.00\pm 0.34$	$2.83\pm 0.97$	$2.86\pm 0.71$	$0.279\pm 0.085$	$0.395\pm 0.097$	$0.328\pm 0.075$	GANDALF
43	UGC07053	185622	0.21		$1.00\pm 0.05$	$1.04\pm 0.03$	$3.09\pm 0.08$	$2.86\pm 0.06$	$0.145\pm 0.021$	$0.233\pm 0.037$	$0.178\pm 0.027$	GFC
43	UGC07053	185623	0.64	$0.74\pm 0.53$	$1.00\pm 0.20$	$0.15\pm 0.15$	$0.67\pm 0.17$	$2.86\pm 0.27$	$0.328\pm 0.213$	$0.614\pm 0.142$	$0.561\pm 0.134$	GFC
43	UGC07053	791635	0.0	$4.10\pm 0.89$	$1.00\pm 0.05$	$0.60\pm 0.02$	$1.72\pm 0.06$	$2.35\pm 0.05$	$0.085\pm 0.025$	$0.258\pm 0.028$	$0.185\pm 0.027$	GANDALF
44	UGC07332	85881	0.0	$86.23\pm 52.80$	$1.00\pm 1.30$	$0.39\pm 0.48$	$1.12\pm 1.37$	$1.62\pm 0.57$	$0.155\pm 1.762$	$1.524\pm 0.572$	$1.116\pm 0.439$	GANDALF
45	UGC07394	221194	0.52	$2.97\pm 6.90$	$1.00\pm 0.19$	$0.37\pm 0.18$	$0.69\pm 0.24$	$2.86\pm 0.24$	$0.302\pm 0.101$	$0.563\pm 0.133$	$0.555\pm 0.134$	GFC
45	UGC07394	221195	0.5	$0.72\pm 0.12$	$1.00\pm 0.07$	$0.82\pm 0.05$	$2.31\pm 0.14$	$2.86\pm 0.11$	$0.143\pm 0.012$	$0.272\pm 0.015$	$0.177\pm 0.013$	GANDALF
46	UGC07396	9163	0.02	$5.82\pm 1.15$	$1.00\pm 0.10$	$0.43\pm 0.02$	$1.24\pm 0.07$	$2.86\pm 0.06$	$0.455\pm 0.050$	$0.590\pm 0.059$	$0.447\pm 0.064$	GANDALF
48	UGC09215	238952	0.7	$2.50\pm 56.85$	$1.00\pm 0.16$	$0.52\pm 0.02$	$1.38\pm 0.03$	$2.86\pm 0.03$	$0.503\pm 0.018$	$0.461\pm 0.014$	$0.318\pm 0.011$	GFC
48	UGC09215	714924	0.0	$43.90\pm 191.72$	$1.00\pm 0.04$	$0.41\pm 0.01$	$1.09\pm 0.03$	$1.97\pm 0.02$	$0.321\pm 0.007$	$0.326\pm 0.006$	$0.234\pm 0.005$	GFC
48	UGC09215	714923	0.43	$2.82\pm 39.15$	$1.00\pm 0.02$	$0.60\pm 0.01$	$1.82\pm 0.02$	$2.86\pm 0.01$	$0.478\pm 0.006$	$0.365\pm 0.008$	$0.267\pm 0.004$	GFC
48	UGC09215	319800	0.13	$1.91\pm 0.07$	$1.00\pm 0.01$	$0.97\pm 0.00$	$2.76\pm 0.01$	$2.86\pm 0.01$	$0.263\pm 0.005$	$0.309\pm 0.005$	$0.223\pm 0.005$	GANDALF
48	UGC09215	319801	0.0	$1.95\pm 0.09$	$1.00\pm 0.02$	$0.40\pm 0.01$	$1.13\pm 0.02$	$1.91\pm 0.01$	$0.272\pm 0.010$	$0.329\pm 0.012$	$0.214\pm 0.010$	GANDALF
49	UGC09299	593645	0.29		$1.00\pm 0.07$	$0.60\pm 0.04$	$1.80\pm 0.07$	$2.86\pm 0.06$	$0.483\pm 0.035$	$0.684\pm 0.033$	$0.474\pm 0.026$	GFC
49	UGC09299	593646	0.0	$2.95\pm 0.24$	$1.00\pm 0.03$	$0.55\pm 0.01$	$1.56\pm 0.03$	$2.82\pm 0.03$	$0.330\pm 0.017$	$0.430\pm 0.018$	$0.308\pm 0.016$	GANDALF
50	UGC09348	619104	0.39	$5.13\pm 0.62$	$1.00\pm 0.07$	$0.43\pm 0.02$	$1.22\pm 0.06$	$2.86\pm 0.04$	$0.637\pm 0.032$	$0.734\pm 0.028$	$0.554\pm 0.030$	GANDALF
51	UGC09432	367146	0.1		$1.00\pm 0.03$	$0.74\pm 0.01$	$2.15\pm 0.03$	$2.86\pm 0.05$	$0.195\pm 0.014$	$0.450\pm 0.018$	$0.313\pm 0.014$	GANDALF
52	UGC09470	16827	0.0	$5.06\pm 0.13$	$1.00\pm 0.01$	$1.21\pm 0.00$	$3.44\pm 0.01$	$2.22\pm 0.01$	$0.103\pm 0.001$	$0.173\pm 0.002$	$0.121\pm 0.002$	GANDALF
53	UGC09482	16863	0.0	$5.18\pm 0.38$	$1.00\pm 0.02$	$0.86\pm 0.01$	$2.45\pm 0.03$	$2.65\pm 0.02$	$0.252\pm 0.011$	$0.404\pm 0.013$	$0.265\pm 0.011$	GANDALF
53	UGC09482	16899	0.0	$6.51\pm 0.69$	$1.00\pm 0.05$	$0.38\pm 0.02$	$1.08\pm 0.05$	$2.49\pm 0.05$	$0.243\pm 0.034$	$0.492\pm 0.039$	$0.342\pm 0.038$	GANDALF
54	UM452	54103	0.21	$2.46\pm 0.07$	$1.00\pm 0.01$	$0.85\pm 0.00$	$2.42\pm 0.01$	$2.86\pm 0.01$	$0.258\pm 0.004$	$0.366\pm 0.005$	$0.266\pm 0.004$	GANDALF
55	UM456	559583	0.0	$1.61\pm 0.12$	$1.00\pm 0.01$	$1.80\pm 0.01$	$5.15\pm 0.02$	$1.93\pm 0.01$	$0.037\pm 0.003$	$0.078\pm 0.003$	$0.053\pm 0.003$	GANDALF
55	UM456	559584	0.0	$4.76\pm 0.16$	$1.00\pm 0.02$	$1.05\pm 0.01$	$3.00\pm 0.02$	$2.06\pm 0.01$	$0.078\pm 0.005$	$0.197\pm 0.006$	$0.138\pm 0.006$	GANDALF
56	UM456A	559608	0.0	$3.65\pm 0.13$	$1.00\pm 0.02$	$1.15\pm 0.01$	$3.30\pm 0.02$	$1.73\pm 0.01$	$0.049\pm 0.006$	$0.125\pm 0.006$	$0.083\pm 0.006$	GANDALF
56	UM456A	559610	0.0	$2.27\pm 0.13$	$1.00\pm 0.02$	$1.10\pm 0.01$	$3.15\pm 0.02$	$2.67\pm 0.02$	$0.060\pm 0.006$	$0.166\pm 0.006$	$0.110\pm 0.006$	GANDALF
57	UM491	290172	0.0	$4.52\pm 0.10$	$1.00\pm 0.01$	$0.87\pm 0.00$	$2.47\pm 0.01$	$2.53\pm 0.01$	$0.201\pm 0.005$	$0.277\pm 0.005$	$0.191\pm 0.004$	GANDALF

Table 12. Table 8: Emission line measurements and derived metallicities for the Dwarf Galaxy Survey using the O3N2, N2, PG16S and KE08/T04 methods. The literature emission lines have been corrected for stellar absorption and reddening using methods in the listed references.

name	$I_{\lambda }/I_{H\beta }$							12+log(O/H)				Ref
	$H\alpha$	$\left[O\text{ii}\right]$	$[O\text{iii},4959]$	$[O\text{iii},5007]$	$\left[N\text{ii}\right]$	$[S\text{ii},6713]$	$[S\text{ii},6731]$	N2	O3N2	PG16S	KE08/T04
Haro II	2.91	1.08	1.19	3.72	0.488	0.156	0.102	8.40	8.30	8.45	8.49	1
Haro 2	2.86	3.41	0.55	1.45	0.461	0.297	0.298	8.39	8.42	8.36	8.65	2
Haro 3	2.86	2.71	1.08	3.22	0.236	0.223	0.180	8.24	8.22	8.24	8.37	1
He 2-10	3.00	2.29	0.36	1.06	0.878	0.301	0.266	8.60	8.55	8.55	8.8	3
HS0822+3542	2.75	0.31	1.19	3.58	0.010	0.026	0.018	7.29	7.78	7.40		4
HS1304+3259	2.86	1.85	1.52	4.51	0.100	0.330a		8.10	8.05	8.03	8.07	5
HS1319+3224	2.86	1.24	1.63	5.03		0.140a						4
HS1330+3651	2.86	0.48	1.64	4.9	0.090	0.350a		8.08	8.03	8.03		4
HS1442+4250	2.76	0.54	1.75	4.98	0.023	0.042	0.029	7.72	7.84	7.73		6
I Zw 18	2.74	0.408	0.636	1.906	0.012	0.029	0.022	7.39	7.88	7.15		7,13
II Zw 40	2.87	0.84	2.46	7.41	0.063	0.067	0.054	8.09	7.92	8.14		8
IC10	2.85	1.01	1.35	3.97	0.241	0.118	0.094	8.25	8.20	8.33	8.33	9
Mrk 1089	2.86	1.50	0.74	2.24	0.315	0.141	0.101	8.30	8.31	8.38	8.51	10
Mrk 1450	2.83	1.35	1.79	4.76	0.067	0.117	0.083	8.02	7.99	7.95		11
Mrk 153	2.81	0.00	1.51		0.059	0.247a		7.99				12
Mrk 209	2.78	0.72	1.96	5.54	0.029	0.061	0.045	7.80	7.86	7.80		13
Mrk 930	2.85	2.37	1.39	4.17	0.143	0.269	0.198	8.15	8.12	8.11	8.19	14
NGC 1140	2.88	2.32	0.97	0.29	0.256	0.231	0.175	8.25	8.57	8.28	8.81	15
NGC 1569	2.84	0.99	1.50	4.51	0.137	0.205	0.147	8.15	8.10	8.13	8.16	16
NGC 1705	2.86	3.74	1.05	3.00	0.105	0.060a		8.10	8.12	7.99	8.2	17
NGC 1705	2.86	3.37	1.66	4.87		0.026a						16
NGC 1705	2.91	2.54	1.47	4.25	0.111	0.034a		8.11	8.07	8.24	8.11	16
NGC 1705	2.76	2.43	1.52	3.83								16
NGC 1705	2.85	4.00	1.03	3.15	0.039	0.049a		7.89	7.97	7.73		16
NGC 1705	2.86	4.75	1.32	3.67	0.122	0.016a		8.13	8.07	8.33	8.18	16
NGC 1705	2.81	2.74	1.67	4.86	0.098	0.037a		8.09	8.04	8.25		16
NGC 1705	2.86	3.12	1.71	4.94	0.069	0.036a		8.03	7.99	8.15		16
NGC 1705	2.84	3.49	1.69	4.87	0.095	0.038a		8.09	8.04	8.24		16
NGC 1705	2.84	4.36	1.45	4.27	0.148	0.047a		8.16	8.12	8.28	8.2	16
NGC 1705	2.83	2.64	1.13	3.40		0.033a						16
NGC 1705	2.86	5.13	0.59	2.04								16
NGC 1705	2.86	4.01	1.09	3.19	0.097	0.043a		8.09	8.10	8.03	8.16	16
NGC 1705	2.86	4.02	1.26	3.59	0.154	0.043a		8.17	8.15	8.23	8.25	16
NGC 1705	2.86	4.08	1.57	4.39	0.122	0.046a		8.13	8.09	8.24	8.14	16
NGC 1705	2.86	3.52	1.63	4.64	0.016	0.038a		7.42	7.80	7.67		16
NGC 1705	2.79	3.02	1.37	3.93	0.062	0.036a		8.01	8.01	8.02		16
NGC 2366	2.86	0.84	1.34	3.93	0.139	0.234	0.167	8.15	8.12	8.08	8.2	18
NGC 2366	2.86	1.75	1.93	5.73	0.037	0.099	0.074	7.87	7.88	7.84		17
NGC 4214	2.85	3.04	1.00	3.03	0.241	0.267	0.194	8.24	8.23	8.23	8.39	15
NGC 4214	2.80	3.08	2.47	7.52	0.041	0.035	0.033	7.91	7.86	8.08		17
NGC 4449	2.87	3.89	0.69	2.07	0.338	0.476	0.334	8.32	8.33	8.22	8.53	15
NGC 4861	2.86	1.41	1.26	3.76	0.204	0.346	0.173	8.22	8.18	8.16	8.3	17

Table 13. Table 8: Continued

name	$I_{\lambda }/I_{H\beta }$							12+log(O/H)				Ref
	$H\alpha$	$\left[O\text{ii}\right]$	$[O\text{iii},4959]$	$[O\text{iii},5007]$	$\left[N\text{ii}\right]$	$[S\text{ii},6713]$	$[S\text{ii},6731]$	O3N2	N2	PG16S	KE08/T04
NGC 5253	2.83	2.60	1.42	4.22	0.200	0.270	0.205	8.21	8.16	8.17	8.28	15
NGC 625	2.86	1.76	1.52	4.53	0.123	0.136	0.101	8.13	8.08	8.10	8.13	19
NGC 625	2.84	2.31	0.99	2.95	0.204	0.197	0.146	8.21	8.21	8.24	8.36	18
NGC 625	2.82	3.38	0.87	2.55	0.262	0.341	0.240	8.27	8.27	8.21	8.45	18
NGC 625	2.86	4.86	0.33	1.03	0.422	0.676	0.520	8.37	8.46	8.2	8.69	18
NGC 6822	2.85	0.94	1.77	5.35	0.051	0.067	0.050	7.96	7.94	7.94		20
NGC 6822	2.85	1.47	1.44	4.26	0.071	0.103	0.072	8.03	8.02	7.92		19
Pox 186	2.86	0.35	2.09	6.22								21
SBS 0335-052	2.86	0.30	1.09	3.24	0.009	0.021	0.020	7.17	7.77	7.33		17
SBS 0335-052	2.86	0.25	1.1	3.29	0.009	0.020	0.017	7.17	7.76	7.35		17
SBS 0335-052	2.86	0.26	1.42	4.27	0.061	0.124	0.084	8.00	7.99	7.87		17
SBS 0335-052	2.86	0.23	1.50	4.49	0.124	0.221	0.215	8.13	8.09	8.09	8.13	17
SBS 1159+545	2.76	0.65	1.29	3.80	0.085	0.187	0.133	8.07	8.06	7.92	8.08	17
SBS 1211+540	2.71	0.80	2.04	6.07	0.041	0.093	0.069	7.92	7.90	7.90		17
SBS 1249+493	2.86	1.24	2.01	5.88	0.047	0.097	0.072	7.94	7.91	7.93		17
SBS 1415+437	2.86	1.18	1.19	3.54	0.037	0.090	0.067	7.87	7.95	7.66		12
SBS 1533+574	2.81	2.46	1.30	3.80	0.122	0.234	0.170	8.13	8.11	8.03	8.18	12
SBS 1533+574	2.85	2.04	1.79	5.33	0.087	0.167	0.117	8.07	8.01	8.05		12
Tol 0618-402	2.86	2.11	1.62	4.95								22
Tol 0618-402	2.86	2.35	1.60	4.92								21
Tol 1214-277	2.74	0.36	1.76	5.28	0.009	0.019	0.016	7.23	7.71	7.58		14
UGC 4483	2.86	1.32	0.90	2.73	0.037	0.070	0.052	7.88	7.99	7.56		17
UGCA 20	2.76	0.92	0.89	2.60	0.035	0.073	0.050	7.87	7.99	7.53		23
UGCA 20	2.76	1.35	0.85	2.58	0.044	0.088	0.064	7.93	8.02	7.58		22
UM 133	2.86	1.81	1.25	3.74	0.043	0.118	0.083	7.91	7.96	7.71		17
UM 311	2.89	1.80	1.32	3.98	0.180	0.167	0.124	8.19	8.15	8.16	8.26	17
UM 448	2.85	2.78	0.87	2.60	0.409	0.366	0.285	8.36	8.33	8.31	8.53	13
UM 461	2.78	0.53	2.04	6.02	0.021	0.052	0.042	7.68	7.80	7.74		13
VII Zw 403	2.83	1.36	1.17	3.52	0.051	0.105	0.077	7.96	8.00	7.74		17

Equations

$${\tau }_{\mathrm{dest}}=\frac{M_g}{m_{\mathrm{ISM}}{R}_{\mathrm{SN}}(t)},$$

$${\tau }_{\mathrm{grow}}=\frac{M_g}{ϵZ\psi (t)}{\left(1-\frac{{\eta }_d}{Z}\right)}^{-1},$$

$$\mathrm{SFR}(t)\propto \frac{t}{{\tau }^2}{e}^{-t/\tau }$$

$$12+\log \left(\frac{\mathrm{O}}{\mathrm{H}}\right)=12+\log \left(\frac{\mathrm{oxygen}\mathrm{mass}/16}{\mathrm{gas}\mathrm{mass}/1.32}\right).$$

$$P(\text{model}|\text{data})=P(\text{data}|\text{model})\times P(\text{model})/P(\text{data}).$$

$$P(d_i|(x_0\&f))\propto \exp \left(-\frac{(x_i-x_0{)}^2}{2{\sigma }_{x_i}^2}-\frac{(y_i-f(x_0){)}^2}{2{\sigma }_{y_i}^2}\right).$$

$$P(d_i|f)\propto \int \exp \left(-\frac{(x_i-x_0{)}^2}{2{\sigma }_{x_i}^2}-\frac{(y_i-f(x_0){)}^2}{2{\sigma }_{y_i}^2}\right)d{x}_0.$$

$$P(d_i|f)=\frac{\int \exp \left(-\frac{(x_i-x_0{)}^2}{2{\sigma }_{x_i}^2}-\frac{(y_i-f(x_0){)}^2}{2{\sigma }_{y_i}^2}\right)d{x}_0}{\int \exp \left(-\frac{(x_i-x_0{)}^2}{2{\sigma }_{x_i}^2}-\frac{(y_{\text{max}}-f(x_0){)}^2}{2{\sigma }_{y_i}^2}\right)d{x}_0}.$$

$$\log (\mathcal{L}(\mathbf{d}|f))=\sum _i\log (P(d_i|f)).$$

$${\chi }_{\text{eff}}^2=\sum _i-2\log (P(d_i|f))$$

$$⟨{\chi }_{\text{eff}}^2⟩=\frac{{\sum }_i-2\log (P(d_i|f))}{n}$$

$$\frac{d(M_d)}{dt}={\int }_{m_{{\tau }_m}}^{m_U}$$

$$\text{mbox}\times \psi (t-{\tau }_m)\varphi (m)dm-(M_d/M_g)\psi (t)$$

$$\text{mbox}-\left(1-f_c\right)\frac{M_d}{{\tau }_{\mathrm{dest}}}+f_c\left(1-\frac{M_d}{M_g}\right)\frac{M_d}{{\tau }_{\mathrm{grow}}}$$

$$\text{mbox}+M_{d,i}+{\left(\frac{M_d}{M_g}\right)}_{I}I(t)-{\left(\frac{M_d}{M_g}\right)}_{O}O(t).$$

Full text

††pagerange: Using dust, gas and stellar mass selected samples to probe dust sources and sinks in low metallicity galaxies–LABEL:lastpage††pubyear: 2017

1 Introduction

Interstellar dust is formed in the winds of evolved low-to-intermediate mass stars (LIMS, Ferrarotti & Gail, 2006; Sargent et al., 2010) and in core-collapse supernovae (SNe) as massive, short-lived stars end their lives (e.g. Dunne et al., 2003; Rho et al., 2008; Dunne et al., 2009; Barlow et al., 2010; Matsuura et al., 2011; Gomez et al., 2012; Indebetouw et al., 2014; Gall et al., 2014). There are also strong indications for grain growth in the Interstellar Medium (ISM) as dust grains acquire additional mass from the gas phase by accretion in both high and low redshift galaxies (Dwek et al., 2007; Mattsson & Andersen, 2012; Asano et al., 2013; Zhukovska, 2014; Rowlands et al., 2014b). Recent surveys with the Herschel Space Observatory (hereafter Herschel, Pilbratt et al., 2010) of nearby galaxies have produced dust mass scaling relations with stellar mass, gas mass and star formation rate in both targeted samples of nearby galaxies, such as the Herschel Reference Survey (HRS, e.g. Boselli et al. 2010; Cortese et al. 2012; Smith et al. 2012; Cortese et al. 2014), and in wide-area blind surveys (Dunne et al., 2011; Bourne et al., 2012; Clark et al., 2015) including the Herschel-Astrophysical Terahertz Large area Survey (H-ATLAS, Eales et al., 2010). The dust properties of the blind, volume-limited, dust-selected HAPLESS sample of 42 galaxies over the equatorial H-ATLAS fields were presented by Clark et al. (2015, hereafter C15).

C15 attempted to model the HRS and HAPLESS galaxies using a simple closed box chemical evolution model and suggested the following: as galaxies evolve, their dust content first rises steeply, then levels off and reaches its peak about half way through its evolution, and finally declines towards lower gas fractions. In De Vis et al. (2017, hereafter DV17), a local Hi-selected sample taken from the same H-ATLAS fields (dubbed ‘HiGH’) was used to complement the stellar mass selected HRS and the dust-selected HAPLESS sample. They showed that the Hi selection recovered similar dust- and gas-rich galaxies as were seen in HAPLESS, but also revealed gas-rich sources with much lower dust content. DV17 showed that these dust-poor sources are offset from the simple evolutionary scenario put forward in C15.

Zhukovska (2014) compared the sample from Rémy-Ruyer et al. (2014) (including the Dwarf Galaxy Survey (DGS, Madden et al., 2013), the largest sample of low metallicity sources observed with Herschel) with a chemical evolution model to show that the observed variation in dust-to-gas ratio and metallicity in local star-forming dwarfs can be explained using models with bursty star formation histories, low dust yields from core-collapse SNe and additional grain growth in the ISM. Feldmann (2015) took the sample from Rémy-Ruyer et al. (2014) and used both an analytic approximation and dynamic one-zone chemical evolution models to fit the observed relationships in the 126 local galaxies. These models require very rapid grain growth, which activates at a critical metallicity, to match the observed dust-to-metal ratio in the galaxies. Feldmann (2015) also argues that there is a balance between metal-poor inflows and enriched outflows which regulates the dust-to-metal ratio. Popping et al. (2016) study the dust content of galaxies from z = 0 to z = 9 using chemical evolution models including stellar dust, dust grain growth, destruction of dust by supernovae and in the hot halo, and dusty winds and inflows.

In this companion paper to DV17, we add additional metallicity information to DV17’s compilation of dust, stellar mass and Hi-selected samples of nearby galaxies, and add the metal-selected DGS, in order to investigate the dust-to-gas and dust-to-metal properties for a total of 382 sources (44 DGS sources, 58 HAPLESS+HiGH sources and 280 HRS sources have dust masses from Herschel photometry). The combined sample here allows one to sample a wider range of gas fractions than possible before (from $0.05<f_{g}<0.97$). We apply a chemical evolution model to interpret the data by relaxing the closed box model assumption from C15 and DV17 and adding inflows and outflows, using different star formation histories (SFH), allowing for dust grain growth in the ISM and dust destruction. Section 2 reminds the reader of the samples from DV17 and introduces the DGS. Section 3 describes the new metallicity data and the used calibrations. Section 4 briefly describes the chemical model and the combination of parameters modelled in this work. The results are discussed in Section 5, where we attempt to determine the contribution from different dust sources and to explain the dust-poor, gas-rich sample first shown in DV17. Our conclusions are listed in Section 6.

2 Nearby galaxy samples

A detailed description of the HRS, HAPLESS and HiGH samples used in this work is provided in detail in DV17. Here we briefly remind the reader of the different datasets and parameters, introduce the new metallicity measurements and introduce the Dwarf Galaxy Survey (Madden et al., 2013; Rémy-Ruyer et al., 2013) which we add to our sample of local Herschel galaxies in Section 4. The average properties for the samples used in this work are shown in Table 1. By compiling the different nearby galaxy samples, we can model the dust properties for a total of 382 sources, compared to 126 sources in Zhukovska (2014) and Feldmann (2015). We also increase the number of low metallicity sources (additional 67 sources with $Z<1/3\,Z_{\odot}$), which lie off the scaling relations for more evolved sources. This is particularly important given the relevance of immature, unevolved low metallicity sources as analogues for the first galaxies. When comparing all samples, $M_{d}/M_{\rm bary}$ first rises steeply, then levels off and then drops again as galaxies evolve from high to low gas fractions.

2.1 Hi, dust and stellar mass selected samples

The dust-selected HAPLESS (C15) is a blind, volume-limited sample of 42 local ($z<0.01$) galaxies detected at 250 $\mu$m from the H-ATLAS Phase 1 Version-3 internal data release, covering 160 sq. degrees of the sky (Valiante et al., 2016; Bourne et al., 2016). The Hi-selected sample (HiGH, DV17) is extracted from the same H-ATLAS area and includes 40 unconfused Hi sources identified in the Hi Parkes All Sky Survey (HIPASS, Barnes & Hernquist 1992; Meyer et al. 2004) and the Arecibo Legacy Fast ALFA Survey (ALFALFA, Giovanelli et al. 2005; Haynes et al. 2011, Haynes et al. priv comm.); 24 of these sources overlap with the HAPLESS sample. DV17 further split their sample by stellar mass into HiGH-high and HiGH-low categories based on whether the sources were above or below $M_{*}=10^{9}\,\rm M_{\odot}$. To supplement the dust and Hi selected samples taken from H-ATLAS, we follow C15 and DV17 and use the HRS (Boselli et al., 2010) which provides a (quasi) stellar mass selected sample of nearby sources. The HRS targeted 323 galaxies ranging from late-type to early type sources. Hi masses were taken from Boselli et al. (2014). DV17 compiled FUV-submm photometry for each of these samples, and subsequently derived dust masses, stellar masses and star formation rates consistently using magphys (da Cunha et al., 2008). The magphys properties for the HiGH  and HAPLESS samples are provided in Table 5.

2.2 The Dwarf Galaxy Survey

In this work, we also include results from the Dwarf Galaxy Survey (DGS; Madden et al., 2013) to improve our sampling of galaxies at low stellar masses and metallicities. The DGS sources were selected from several other surveys in order to make a broad sample of 50 galaxies ranging from very low ($\sim 1/50Z_{\odot}$) to moderate metallicity ($\sim 1/3Z_{\odot}$). In order to compare the samples, we need consistent methods to calculate the different galactic properties. Unfortunately we do not have the same complete UV-submm coverage for DGS sources as we have available for the H-ATLAS and HRS. Consequently, we redetermined the dust masses for the DGS galaxies using a two-component modified blackbody (MBB) fit to the 70-500 $\mu$m photometry provided in Rémy-Ruyer et al. (2013; 2015). This method produces consistent results with the dust masses output by magphys for the HAPLESS and HiGH sources with dust temperatures $>$15 K and both methods assume the same dust absorption coefficient of $\kappa_{850}=0.07\,\rm{m^{2}\,kg^{-1}}$ (James et al., 2002). However, for some sources fitted by a MBB with $\rm T_{c}<15K$, magphys results in warmer temperatures (by $\sim$3 K) and therefore smaller dust masses (see also DV17). After scaling the Rémy-Ruyer et al. (2015) masses for graphite grains by the difference in $\kappa$ used in their work and magphys, these dust masses are entirely consistent with the MBB results for all sources with $\rm T_{c}>15K$. For some of the colder sources there is an offset, yet this is of the same magnitude as the offset between magphys and the MBB method. Therefore in what follows, we simply scale the Rémy-Ruyer et al. (2015) dust masses for the difference in $\kappa$ to be consistent across samples.

Rémy-Ruyer et al. (2015) derived stellar masses based on the Eskew et al. (2012) method. To check consistency with the magphys stellar masses, we re-derived stellar masses for our HiGH sample using their calibration and found that the DGS stellar masses were a factor of $\sim 3.2$ larger than magphys. This difference stems mainly from Eskew et al. (2012) adopting a Salpeter initial mass function (IMF), whereas in this work we assume a Chabrier IMF. After scaling the DGS values to be consistent with the other samples here, the HiGH-low and DGS contain galaxies with similar average stellar masses. SFRs for DGS were taken from Rémy-Ruyer et al. (2015) and were estimated using a combination of $L_{\rm TIR}$ and the observed H$\alpha$ luminosity (Kennicutt et al., 2009). As there are no integrated H$\alpha$ luminosities available for the HiGH and HAPLESS samples, we compared this SFR method with the SFRs output by magphys for the HRS galaxies. We found that these methods were compatible for all but the most quiescent sources in the HRS (specific star formation rate $<10^{-11}\rm{\,M_{\odot}\,yr^{-1}}$) which are not discussed here. Hi masses are available for 35 DGS galaxies from Madden et al. (2013).

3 Metallicities and calibrations

To calculate metallicities for the HiGH and HAPLESS samples, we use fibre optical spectroscopy from SDSS (Thomas et al., 2013), supplemented by GAMA (Hopkins et al., 2013) (v17). Although GAMA is an extragalactic survey of thousands of galaxies, we have used GAMA fibre spectra that, for our nearby galaxies, correspond to Hii regions within the galaxies. Emission lines were measured by running each spectrum through a modified version of the Gas AND Absorption Line Fitting algorithm (GANDALF; Sarzi et al., 2006). Results were cross checked with GAMA’s GaussFitComplexv05 (GFC; Gordon et al., 2016) and both techniques gave comparable results for all but 15 Hii regions. The results for these 15 Hii regions were checked against the Fit3D (Sánchez et al., 2016) and the GAMA SpecLines (v4) catalogues (Hopkins et al., 2013) and found to be consistent with the GFC method.

For many of the HiGH and HAPLESS sources, we found multiple fibres within the same galaxy. Star forming (Hii) regions were selected using the criteria in Kauffmann et al. (2003) by placing sources on the BPT diagram (Baldwin et al., 1981). This resulted in a sample of 95 Hii regions for the 40 HiGH galaxies and 85 Hii regions for the 42 HAPLESS galaxies (67 Hii regions overlap as their galaxies are in both samples). The emission line fluxes for each Hii region were corrected for stellar absorption, and for internal and galactic extinction using the Balmer decrement $C(H_{\beta})$ and the Cardelli et al. (1989) dust obscuration curve. Errors on the line measurements were provided by GANDALF or GFC. We then bootstrapped the measurements by generating 1000 new emission line fluxes assuming a normal distribution with the extinction-corrected emission line fluxes as mean and the measured error as the standard deviation of the distribution. The HiGH and HAPLESS emission lines and their errors are presented in Table 7. For the HRS, emission lines from integrated spectroscopy are available from Boselli et al. (2013) for 170 LTGs. DGS line measurements are taken from the literature (Table 8; S. Madden, *priv. comm.*111We include the DGS emission lines and metallicities for the community in Table 8, and we correct some of the references.).

To derive metallicities from the emission line spectra, we compared the results from different empirical and theoretical methods in order to understand any systematic differences that may result from our methods. Here, our aim is to compare the different calibrations to determine which one fits our model best. Empirical calibrations are only valid for the same range of excitation and metallicity as the Hii regions that were used to build the calibration. Since they are determined assuming an electron temperature, these methods may systematically underestimate the true metallicity if there are temperature inhomogeneities in a galaxy. This is thought to be more severe in metal-rich Hii regions because the higher efficiency of metal-line cooling leads to stronger temperature gradients (Garnett, 1992; Stasińska, 2005; Moustakas et al., 2010). On the other hand, theoretical calibrations require inputs including stellar population synthesis and photoionization models; often the theoretical metallicities are higher than those found with the empirical calibrations.

We therefore chose to compare three common empirical methods including O3N2 and (third order polynomial) N2 from Pettini & Pagel (2004), and the $S$ calibration from Pilyugin & Grebel (2016, hereafter PG16S). All three methods produce metallicities that correlate well with stellar mass and gas fraction (e.g. Kewley & Ellison, 2008). However, the O3N2 calibration is only calibrated for metallicities $12+\mathrm{log(O/H)}>8$ (Pettini & Pagel, 2004; Marino et al., 2013), and the N2 method also runs into difficulties at the lowest metallicities due to the large scatter observed in $\mathrm{N/O}$ ratios (Morales-Luis et al., 2014) and instead provides upper limits to the true metallicity for galaxies when $12+\mathrm{log(O/H)_{N2}}<8$. We also derived metallicities consistent with the theoretical calculations from Tremonti et al. (2004, hereafter T04). As we do not have access to their code we used the scaling relation between O3N2-T04 from Kewley & Ellison (2008), calibrated against 27,730 SDSS star-forming galaxies (hereafter KE08/T04). We note that this conversion is only valid for $8.05<12+\mathrm{log(O/H)_{O3N2}}<8.9$. Additionally, we have determined metallicities using the Bayesian-based IZI tool (Blanc et al., 2015) which also provides a theoretical calibration based on photo-ionisation models. The IZI results were found to be entirely consistent with the KE08/T04 calibration and are thus not further listed in this work. When the different calibrations are compared, we find the PG16S calibration produces lower metallicities, followed by N2, 03N2 and then KE08/T04 (see also Table 3). Because the limited validity of the other calibrations below $12+\mathrm{log(O/H)}=8$, PG16S is the most reliable calibration for the low metallicity sources. Given the importance of low metallicity sources in this work, we will plot metallicities using the PG16S calibration throughout. However, for high metallicity sources, there is a remaining uncertainty in the metallicity relations in this work with respect to which calibration is used, and we therefore highlight the main differences throughout the text and include results derived from all three calibrations where appropriate.

Metallicities for each HiGH and HAPLESS galaxy were derived from a weighted average of the multiple Hii regions within the same source (Table 6). Errors were derived by adding in quadrature the bootstrapped values on the extinction-corrected emission lines and the intrinsic scatter observed between the different Hii regions within the same galaxy. The latter amounts to an uncertainty of 0.06 dex (see also Bresolin & Kennicutt, 2015). Note that these metallicities are based on an average of multiple small (2*′′* for GAMA, 3*′′* for SDSS) fibres per source and not on integrated measurements. They are thus not ideally suited for extended sources, yet they at least provide a good first estimate for the metallicity of the galaxies in our sample. The calibrations used in this work are derived using the electron temperature method. The uncertainty in the absolute metallicity determination by this method is $\sim 0.1$ dex (Kewley & Ellison, 2008). We thus add 0.1 dex in quadrature to the uncertainty on the averaged metallicity for each galaxy and each calibration. The resulting metallicities and uncertainties for HiGH and HAPLESS are listed in Table 6.

For the HRS and DGS, we simply derive metallicities based on the integrated spectroscopy for each of the three calibrations. The DGS metallicities were originally estimated using the Pilyugin & Thuan (2005, hereafter PT05) calibration (Madden et al., 2013). PT05 is calibrated over a similar range of metallicity to N2 and PG16S, but there are a number of reasons we choose not to use this as a method to determine metallicities in this study. First, PT05 is not a good estimator for metal-rich galaxies that have low excitation parameters $P$ and high values of $R_{23}$ (Moustakas et al., 2010), such as the HRS and HiGH-high galaxies. PT05 is therefore more suited for the DGS and HiGH-low sources that have appropriate $P$ and $R_{23}$ values but this means we cannot apply a consistent method to derive metallicities across the different samples of nearby galaxies. Second, PT05 metallicities have been shown to have a lot of scatter with stellar mass compared to other calibrators (Kewley & Ellison, 2008), suggesting it is not a good tracer of metallicity across a wide range of galaxy properties. Third, the PT05 method has two ‘branches’ of metallicity values versus the $R_{23}$ emission line ratio with a transition region between the two branches. Because of this, a large difference in $Z$ can be derived for galaxies with very small changes in emission line ratio. The PG16S calibration also uses different relations for high and low metallicities. However the appropriate ranges where the high- and low-metallicity relations can be used, overlap for adjacent metallicities, and the transition zone thus disappears. Fig. 1 compares the DGS metallicities derived here with the published values from Madden et al. (2013, M13), which are based on the PT05 calibration. We find, on average, lower metallicities for galaxies with $12+\mathrm{log(O/H)_{M13}}<8$, and higher metallicities for high metallicity souces (though with less scatter). We see that the differences between PT05 and PG16S are less pronounced than for the other calibrations, though using PG16S still suggest higher metallicities (0.09 dex on average) than previously published, especially for low metallicity sources. We thus use PG16S metallicities throughout this work.

4 The chemical evolution model

A chemical and dust evolution model can be used to build a consistent picture of how the metals, dust and gas content change as galaxies evolve (Tinsley, 1980). The simple chemical evolution model used in C15 to interpret the different scaling relations for dust, gas and stellar mass selected samples neglected dust destruction and grain growth, and assumed that the system was a closed box (no inflows or outflows). Following Zhukovska (2014) and Feldmann (2015), we relax all of these assumptions. The chemical model is presented in full in Rowlands et al. (2014b; see also Morgan & Edmunds, 2003) and the Python code used is freely available on github222https://github.com/zemogle/chemevol. The equation for the dust mass evolution is given in Appendix B. In short, the model uses a prescription for the Star Formation History (SFH) and a Chabrier (2003) IMF to calculate how much of the initial gas is converted into stars at any given time. The model also tracks the continuous build-up of metals as stars end their lives, though metals can be removed in outflows of material. For dust, the picture is more complex. Dust is produced by supernovae and evolved low-intermediate mass stars, and additional mass is gained from the ISM by dust grain growth. Dust is primarily destroyed by SN shocks and astration (the removal of gas and dust due to star formation). We use a one-zone chemical evolution model, i.e. we study the integrated properties of galaxies without spatial resolution, and assume instant mixing of dust, gas and metals.

In this model we include simple analytical prescriptions for grain growth and dust destruction via shocks as described in Rowlands et al. (2014b). The timescale for dust destruction ($\tau_{\rm dest}$, following Dwek et al. 2007) is described as a function of the rate of SN ($R_{\rm SN}$):

[TABLE]

where $M_{g}$ is the gas mass of the galaxy and $m_{\rm ISM}$ is the mass of ISM that is swept up by each individual SN event. In some models (Table 2) we set this to $m_{\rm ISM}=100\rm\,M_{\odot}$, indicative of SN shocks ploughing into typical interstellar densities of $10^{3}\,\rm cm^{-3}$ (Gall et al. 2011; Dwek & Cherchneff 2011), although we also explore models with $1000\,\rm M_{\odot}$ (Dwek et al., 2007), consistent with dust destruction in the diffuse ISM and possibly more appropriate for a low metallicity ISM.

The grain growth prescription is taken from Mattsson & Andersen (2012) where the timescale for dust growth is given by:

[TABLE]

and $\eta_{d}$ is the dust-to-gas ratio, $Z$ is the metallicity ($Z=M_{\rm metals}/M_{\rm gas}$) and $\epsilon$ is a free parameter, which is set to $\epsilon=500$ in Mattsson & Andersen (2012), appropriate for accretion timescales of $10^{7}\,\rm yr$ for a galaxy similar to the Milky Way.

We also test four ‘representative’ star formation histories, including a Milky Way-type exponentially declining SFR (Yin et al., 2009), and two versions of a delayed SFH as parameterised by Lee et al. (2010):

[TABLE]

where $t$ is the age of the galaxy and $\tau$ is the star formation timescale. First, we assume a SFH with $\tau=6.9\rm\,Gyr$ with peak SFR $4.4\,\rm M_{\odot}\,yr^{-1}$ in order to produce the same stellar mass as the Milky Way-type SFH. The second delayed SFH is reduced by a factor of 3, and has $\tau=15\rm\,Gyr$ (see Section 5.3). Finally, a model including a bursty SFH, similar to that used in Zhukovska (2014) to explain the SFR properties of the DGS sources, is also included. In Section 5, we test various parameter combinations, including changing SFHs, IMFs, inflows, outflows and including different dust sources, in order to interpret the observed dust, metal, gas and star formation rates of the nearby galaxy samples. The parameters for Models I-VII, which are good representations for the sampled parameter-space, are listed in Table 2.

We note that this model differs from Rowlands et al. (2014b) in the following ways: (i) the initial remnant mass function is updated. (ii) We now take into account the formation of a black hole for stars with initial mass $m_{i}>40\,\rm M_{\odot}$ when accounting for gas and metals released into the ISM. Stars with progenitor mass above this cut-off mass only contribute gas and metals lost via stellar winds before the collapse. (iii) We add an additional term $f_{c}$ to account for the fraction of gas that is cold enough for grain growth in the ISM. We follow Mancini et al. (2015) and Inoue (2003) by setting this equal to 0.5. This parameter is likely to be higher at earlier times (e.g. Popping et al. 2014; Nozawa et al. 2015) though we choose to keep it constant here. (iv) We no longer interpolate the yields from stars of a given mass but just choose the nearest neighbour value, this has a small effect on the resulting stellar yields. (v) We directly input the dust masses for core collapse SN for stars with initial mass $8.5<M_{i}\leq 40$ from Todini & Ferrara (2001). Rowlands et al. (2014b) used the Todini & Ferrara (2001) dust masses to estimate a condensation efficiency for SN dust ($\delta_{\rm SN}$) and applied that to the metal yields from Maeder (1992). Using the former technique reduces the dust mass by a factor of $\sim$1.8 for a MW-like galaxy at early times ($\rm<0.8\,Gyr$) compared to the latter.

5 Results

5.1 A simple model fit to dust in nearby galaxies

In Fig. 2, we follow C15 and DV17 and compare the evolution of the dust-to-baryonic mass ratio ($M_{d}/M_{\rm bary}$) with gas fraction for the different nearby galaxy samples compiled here. This plot is an excellent starting point as it tracks the relative changes in dust mass in terms of the evolutionary state. We define the baryon mass and gas fraction as $M_{\rm bary}=M_{g}+M_{*}$ and $f_{g}=\frac{M_{g}}{M_{*}+M_{g}}$ respectively, where $M_{g}=1.32\ M_{\text{H{\sc i}}}$ to take into account the mass of neutral helium. Due to the difficulty in obtaining CO detections for all the different samples considered here, particularly for low stellar mass sources, we do not take into account any molecular component. We refer to Section 5.9 for further discussion, though we note here that $\rm{H_{2}}$ does not dominate the total gas mass for our samples and thus including $\rm{H_{2}}$ would not affect the conclusions reached in this work.

In Fig. 2, we find $M_{d}/M_{\rm bary}$ follows a tight relation at low gas fractions. However at high gas fraction there is more scatter, at least in part due to differences in the contributions from the different dust sources. We also show how the observations from the different samples compare with a chemical evolution track similar to C15 and DV17. This model uses a SFH consistent with the Milky Way (Yin et al., 2009), though here we use our updated code (Model I, Table 2). Model I overlaps with Model II in Fig. 2 (see also Section 5.2). Although galaxies are more complex than this simple model, Model I does explain the overall trend in these samples, yet not all sources at gas fractions $<50$ per cent are well-matched. We note that our model peaks at a lower gas fraction ($\sim 0.3$) than in C15 and DV17 due to the changes made to the assumptions and dust inputs described in Section 4. Indeed, as our model has less dust injection from supernovae but the same dust injection from low-intermediate mass stars (LIMS) compared to Rowlands et al. (2014b), this shifts the peak $M_{d}/M_{\rm bary}$ towards lower gas fractions. In this work we assume a dust condensation efficiency for LIMS of 0.16, consistent with predictions of Morgan & Edmunds (2003). This value is somewhat lower than the high condensation efficiencies from theoretical models of dust formation in stellar winds (Zhukovska et al., 2008; Ventura et al., 2012). By choosing an even lower value for the dust condensation efficiency in LIMS we could obtain a better fit to $M_{d}/M_{\rm bary}$ at low gas fractions for the closed box model of C15. However, as we will show in Section 5.2, an equally good fit to $M_{d}/M_{\rm bary}$ can be obtained through the introduction of inflows and outflows when we relax the assumptions of the closed box model.

In Fig. 2, we see that a large fraction of the highest gas fraction galaxies ($f_{g}>85$ per cent, HiGH-low) have significantly lower $M_{d}/M_{\rm bary}$ than expected from Model I. We note, however, that the dust mass of these galaxies have large error bars due to poorly constrained dust temperatures from the magphys fitting. In order to ensure the offset in $M_{d}/M_{\rm bary}$ for these sources is not due to this, we stacked the MIR-submm fluxes for the 8 HiGH-low sources with poorly constrained temperatures. The resulting stacked SED is well fitted by a single modified black body curve with dust temperature $T\sim 35$ K. The lower dust masses for these sources are therefore consistent with them having warmer dust temperatures than the HAPLESS and HRS sources (on average). We conclude that a different set of chemical model properties are necessary to explain this slower build-up of dust for these high gas fraction sources compared to the dust-rich HAPLESS galaxies at the same $M_{d}/M_{\rm bary}$.

Note that in Fig. 2 we also highlight the well-studied galaxy I Zw 18 (part of the DGS sample) thought to be a local analogue of low-metallicity, high-redshift systems (e.g. Herrera-Camus et al. 2012; Fisher et al. 2014). The location of this source on this $M_{d}/M_{\rm bary}$ ‘scaling relation’ (and in later Sections) is indicated by the black diamond using the measured properties from Fisher et al. (2014). As we have re-evaluated the DGS measurements to be consistent across all samples (Section 2), we have indicated where this galaxy moves with our revised measurements (dashed line). We will see in later sections that the dust properties of I Zw 18 are entirely consistent with its gas fraction and metallicity.

5.2 Relaxing the closed box assumption

Fig. 2 also compares the $M_{d}/M_{\rm bary}$ of these samples with different chemical evolution tracks including different SFHs and/or relaxing the closed box assumption from Model I (Models II-VI, Table 2). There are significant differences between some of the models and the data, especially at $f_{g}\sim 80$ per cent. Even for the same gas fraction, nearby low $M_{*}$ galaxies split into two categories: dust-rich and dust-poor and require different chemical evolution models to explain their dust-to-baryonic mass properties. Briefly, we see that the dust-rich low $M_{*}$ sources are matched relatively well by Models I-III, which show a steep rise in $M_{d}/M_{\rm bary}$ at the highest gas fractions ($f_{g}>95$ per cent) and correspond to core-collapse SNe producing $0.17-1.0\,\rm M_{\odot}$ of dust per explosion when there is no dust destruction or grain growth (or the net interstellar grain growth is matched by equal dust destruction). For the sources with high $M_{d}/M_{\rm bary}$ for their gas fraction, an increased SN dust yield results in a better fit. In this case the dust contribution from LIMS needs to be reduced. Even though the SFH for models I and II are very different, their chemical evolution tracks in Fig. 2 nearly overlap, indicating the $M_{d}/M_{\rm bary}$ evolution is not dependent on the SFH for models without dust grain growth.

To model the dust-poor HiGH-low and DGS sources, we follow Zhukovska (2014) and Feldmann (2015) and relax the closed box assumption, reduce the contribution from supernova dust, and include dust grain growth in our model. Models IV-VI therefore require a reduction in the dust production in SNe by a factor of 6-100 compared to the models required to fit the HRS, HiGH-high and HAPLESS. In contrast to our approach (and Zhukovska, 2014), Feldmann (2015) even uses reduced supernova dust yields for sources that are not dust poor given their metallicity and instead uses extremely fast (timescale of $\sim 5$ Myr) grain growth to achieve high dust masses at high gas fractions. There is thus a degeneracy between using a significant contribution from supernova dust, and using very fast dust grain growth. Their grain growth timescales of $\sim 5$ Myr are much faster than typically found in nearby galaxies (Mattsson & Andersen, 2012; Mattsson et al., 2014) or from basic theoretical estimates of the underlying growth rate (Draine, 2009).

At late times (low gas fractions), Models I and II overestimate the amount of $M_{d}/M_{\rm bary}$ and require (stronger) inflows and dust-rich outflows of gas or a reduced dust contribution from LIMS to explain the observed properties. The choice of Models I-VI will be motivated in Sections 5.3-5.6.

5.3 Star formation histories

Next we attempt to explain the observed SFR properties with these models by comparing the change in ${\rm SFR}/M_{\rm bary}$ with gas fraction. Fig. 3 compares the HAPLESS, HRS, HiGH, and DGS samples. In the high gas fraction regime ($f_{g}>80$ per cent), we see that Model I overpredicts the ${\rm SFR}/M_{\rm bary}$, particularly in comparison to the HiGH-low sources. Delayed SFH models provide a closer match to this sample (as used in Models II-VI) by reducing the SFR per unit baryonic mass at early evolutionary stages. The values of the delayed SFHs in Eq. 3 were chosen to match the data in Fig. 3, with Model VI providing a good fit to most333Three HiGH-low sources actually have a higher SFR, more in line with the actively star-forming DGS sources rather than the other normal star-forming HiGH galaxies. of the HiGH-low sources. In models with strong outflows but no inflows (Model III), the baryonic mass is significantly reduced at low gas fractions, and therefore ${\rm SFR}/M_{\rm bary}$ increases as the gas fraction decreases. Model III thus poorly matches the observed ${\rm SFR}/M_{\rm bary}$ at low gas fractions and can be discarded as an unrealistic model. However, when the outflow is matched by an equal inflow as in Model V, $M_{\rm bary}$ stays constant and we find the same ${\rm SFR}/M_{\rm bary}$ track as for the same model without inflows and outflows (i.e. Models II, IV and V overlap in Fig. 3).

The DGS sources lie significantly above the HRS, HiGH and HAPLESS samples in Fig. 3, with higher ${\rm SFR}/M_{\rm bary}$ for the same gas fraction. DGS tends to contain more actively star-forming galaxies (average SFR $0.21\,\rm M_{\odot}\,yr^{-1}$, Table 1) than is typical of nearby dwarfs (e.g. Hunter et al. 2012). Their selection towards more star-forming, low-stellar mass systems could be a consequence of their original selection of galaxies with moderate to very low PT05 metallicities. We return to this in the next section. The intensely SF nature of the DGS was highlighted in Zhukovska (2014), where they found they required bursty SFRs to fit the gas and dust properties of these dwarf galaxies. Even with the revised dust masses and metallicities and the different model assumptions in this work, we also require a bursty SFH to fit the DGS properties (Appendix C). This demonstrates that despite having similar stellar masses, dust temperatures and gas fractions as the HiGH-low sources, the DGS are more actively star forming than the HiGH galaxies and do not appear to be the same sources at a different evolutionary stage. However, we cannot rule out that DGS and HiGH-low are both part of the same evolutionary sequence, with DGS sources undergoing a burst and HiGH-low sources in a quiescent period between bursts. The HiGH-low and HAPLESS samples therefore complement the DGS and provide additional, new, information of more normal star-forming systems at low metallicities, high $f_{g}$, and potentially different dust properties.

5.4 Metallicity build-up

We next wish to compare how the metallicity of galaxies changes as they evolve from high to low gas fractions. The chemical evolution code traces both the total metal mass fraction $Z$ and the oxygen mass, we can directly convert the models to oxygen abundance using:

[TABLE]

In Fig. 4 we see in both the model behaviour and the observations that, in general, the metallicity increases monotonically as galaxies evolve from high to low gas fractions. The models are almost indistinguishable at gas fractions $>80$ per cent in this parameter space. At low gas fractions, Models I and II clearly overestimate the amount of metals; we find models with moderate ($2.5\times$ SFR) outflows of enriched gas and metal-poor inflows are necessary (Models V-VI). Here we note that it is possible that empirical calibrations such as PG16S are underestimated (particularly at low gas fractions) due to temperature inhomogeneities. If this is the case, a theoretical calibration such as KE08/T04 would be more applicable and less strong inflows and outflows would be sufficient.

Fig. 4 also shows that the DGS appears to have lower metallicities than the HRS at low gas fractions and, to a lesser extent the HiGH-low sources at high gas fractions. In other words, the DGS galaxies are, on average, more metal-poor given their evolutionary state. In the previous section we also found DGS are, on average, more actively forming stars. Selecting galaxies ranging from low to moderate metallicity at a given gas fraction appears to result in a sample selection biased towards galaxies with very high SFRs due to the mass-metallicity-SFR relation (Mannucci et al., 2010; Lara-López et al., 2010). Additionally, higher SFR (and thus brighter) sources are easier to observe with Herschel, and it is easier to obtain high signal-to-noise spectra (in order to determine metallicities). This is another reason DGS consists mainly of high SFR sources. As suggested in Feldmann (2015), the low metallicities at a given gas fraction for DGS sources, requires the addition of strong inflows and outflows to regulate the build-up of metals in the DGS galaxies. Only Model VII (Fig. 8), with bursty SFH and stronger inflows/outflows ($4\times$ SFR) than the models (V and VI) used to match the other nearby galaxy samples in this work, can be used to explain the $Z-f_{g}$ properties of the DGS sample.

5.5 Dust-to-gas ratio

Next we compare metallicity with the dust-to-gas ratio for the 253 galaxies in our combined sample that have metallicities available (Fig. 5). The $M_{d}/M_{g}$ ratio correlates with the gas-phase metallicity over a wide range $7.3<12+{\rm log(O/H)_{PG16S}}<9.0$, yet we again identify two regimes. If dust traces the metals or a constant fraction of metals remains in dust grains, we expect a linear $M_{d}/M_{g}-Z$ relationship, with a slope as for Models I-III. Models I-III are consistent with the slope of those galaxies with highest $M_{d}/M_{g}$ in the metallicity range $7.3<12+{\rm log(O/H)_{PG16S}}<8.2$, but the HRS sources do lie offset from these models444Here we do not aim to find a single model that explains the HRS galaxies, rather we simply show how models without grain growth evolve in this plot.. This could be explained either by increasing the dust produced by stars by a factor of 5 or more which would move Models I-III up the y-axis whilst keeping the slope constant. However we note that the amount of dust formed in LIMS stars and SNe is already substantial, and these models have no dust destruction suggesting that adding more dust produced from stars in this way is unrealistic. Alternatively, one could add interstellar grain growth which would act to steepen the ‘tips’ of Models I-III at metallicites $12+{\rm log(O/H)_{PG16S}}>8.2$ (see for example the tips of Models IV-VI). We return to how this may be a result of different dust-to-metal ratios in Section 5.6.

The dust-poor high gas fraction sources (HiGH-low and some of the DGS galaxies) lie well below the linear trend from Model I-III. This offset was already discussed in Rémy-Ruyer et al. (2013; 2015); Zhukovska (2014); Feldmann (2015), who explained this by suggesting the supernova contribution to the dust budget needs be reduced and a dust grain growth term added. The argument is such: for the highest gas-fraction galaxies in Fig. 5 the dust mass needs to be significantly suppressed without reducing the metals. The only way555Note the dust destruction caveat in Section 5.9. to do this is to reduce the amount of dust formed by stars (including SN) in each stellar population. As the dust-to-gas ratio is already lower than expected from a linear trend at high gas fractions, this suggests the SN dust production must be suppressed. The observed dust-to-gas ratio in the lowest metallicity HiGH-low and DGS galaxies requires models with a maximum of $0.01-0.16\,\rm M_{\odot}$ (Models VI-IV) of dust per core collapse SNe, which corresponds to a condensation efficiency of 0.2-3.2 per cent for a 25$\rm M_{\odot}$ progenitor (assuming 5$\rm M_{\odot}$ of metals ejected and that all of this mass can be condensed into dust).

Therefore Models IV, V and VI include a reduced SN dust component (by a factor of 6-100 in mass, Table 2) compared to the MW model. Since there is less stardust in these models, if we require galaxies to ultimately reach the typical dust-to-gas ratios observed at low $f_{g}$ (Fig. 5), we need to also include interstellar grain growth. This dust source is strongly metal-dependent and only becomes important once the galaxy reaches a critical metallicity (Asano et al., 2013). This means that different values of the grain growth parameters $\epsilon$, and consequently $\tau_{\rm grow}$, move the model tracks. An increase of $\epsilon$ steepens the slope of $M_{d}/M_{g}$ (shown by Models IV-VI as they reach the end of their tracks); any offset from the linear trend in Fig. 5 can therefore be mitigated by changing $\epsilon$ such that grain growth starts at a lower metallicity (thereby increasing the dust-to-gas ratio). Alternatively, offsets in Fig. 5 can also be explained through the use of different bursty SFHs, because long quiescent phases allow accretion of existing metals after short active enrichment episodes (Zhukovska, 2014).

The relative contributions to the dust mass budget for Models IV-VI are displayed in Fig. 6. At high gas fractions, stellar sources dominate (mostly SN dust, Rowlands et al., 2014b, C15), yet dust grain growth becomes the largest source of dust mass at gas fraction below 0.88, 0.79 and 0.53 for Model VI, V and IV respectively. The metallicity at which dust grain growth exceeds dust production from stars in our model is reached between $0.003<Z<0.012$ (or $\rm 7.97<12+log(O/H)<8.63$, or $0.88>f_{g}>0.53$), though low values in this range result in the best match with observations. High values of $\epsilon$ (Table 2) lead to short grain growth timescales and low critical metallicities. By the time Models IV-VI reach the lowest gas fractions, dust grain growth produced 70-93 per cent of the total dust mass created over the galaxy’s lifetime.

The ($Z$-dependent) dust grain growth timescales for Models IV, V and VI are shown in Fig. 6 (bottom). For Model IV and V, the dust grain growth timescale $\tau_{\rm grow}$ decreases steeply at high gas fractions (when the critical metallicity is reached), and decreases gradually after that. For Model VI, we again find an initial steep decrease in $\tau_{\rm grow}$ at high gas fractions, yet afterwards we find some fluctuation and then an increase towards the lowest gas fractions. $\tau_{\rm grow}$ is inversely proportional to the SFR (Eqn. 2) and the Model VI SFR decreases steeply towards low gas fractions (and is low in general). This explains the higher $\tau_{\rm grow}$ of Model VI compared to Model V, even though $\epsilon$ is higher. The fluctuation in the $\tau_{\rm grow}$ for Model VI is thus a result of the balance of the growing efficiency of grain growth as the metallicity increases and the SFH reaching a peak and decreasing steeply towards low gas fractions. We note that even though the dust grain growth is slower in Model VI than Model V, it is still faster relative to the SFR and thus a more important term in the chemical evolution.

5.6 Dust-to-metal ratio

The variation in the dust-to-metals ratio is discussed in Mattsson et al. (2014), where they show there is only a small change in $M_{d}/M_{Z}$ (but increased scatter) observed in low metallicity environments (De Cia et al., 2013) and at high redshifts (Zafar & Watson, 2013) even down to $Z<1$ per cent of Solar. 66footnotetext: The normalisation in Feldmann (2015) is given by their model $M_{d}/M_{Z}$ at solar metallicity.In Fig. 6, we find that for HRS, and a small fraction of the other sources, there is indeed not much variation in the $M_{d}/M_{Z}$777To estimate the total metal mass $M_{Z}$ from the observed oxygen abundance from Section 3, we assume $12+{\rm log(O/H)}_{\odot}=8.69$ and a Solar metal mass fraction $Z_{\odot}=0.014$ following Asplund et al. (2009).. The HRS is in good agreement with the MW value (Clark et al., 2016) and the recent survey by Davies et al. (2014) using Herschel observations of galaxies in the Virgo Cluster. Models with stardust only (Model I) predict an almost constant dust-to-metals ratio888The kink in Model I at $\rm{12+log(O/H)}\sim 8.4$ is due to an increasing metal mass from stars resulting from changing the input metal yield file (Maeder, 1992)..

However, there are also $\sim 25$ low metallicity (DGS and HiGH) sources for which $M_{d}/M_{Z}$ is significantly smaller. We note this result is independent on which metallicity calibration was used. Results for N2 and T04/KE08 are included in Table 3 for comparison with PG16S. We thus use our larger and more normal-star forming sample at low $Z$ to further support the Feldmann (2015) result that the dust-to-metal ratio varies as a function of metallicity. The location of the low stellar mass samples (HiGH-low and DGS galaxies) is contrary to what we would expect if stellar sources were the dominant source of dust in the galaxies, which again shows we cannot model these sources without grain growth. Feldmann (2015) attributes the rising dust-to-metal ratio to dust grain growth becoming more efficient as galaxies reach their critical metallicity. In contrast to the strong inflows and outflows, and the extremely efficient interstellar grain growth (timescale of $\sim 4$ Myr) from Feldmann (2015), we find we can also model these sources with more moderate inflows and outflows ($2.5\times$ SFR), and moderate grain growth (timescale of 1 Gyr - 200 Myrs) models (Model IV-VI). The dust growth timescales in our models are more similar to those quoted for the MW and local galaxies (Draine, 2009; Asano et al., 2013; Mattsson & Andersen, 2012; Mattsson et al., 2014).

We note it is very difficult for a model with SFH consistent with the MW, with dust from LIMS combined with significant dust production in supernovae and no dust destruction or grain growth (Model I, peak $M_{d}/M_{Z}\sim 0.2$) to reach the observed MW dust-to-metals ratio ($\sim 0.5$; orange star in Fig. 6) as it evolves. This issue demonstrates why significant interstellar grain growth is needed to supplement the dust mass and reduce the large offset in the predicted $M_{d}/M_{Z}$ in Model I compared to the observed values, even in our own galaxy.

5.7 Statistical constraints

We next attempt to check whether the comparison between the data and Models I-VI in earlier Sections provide strong constraints on the physical properties of these galaxies given the degeneracies in the model. The scatter in the observed values between the different galaxy samples compiled in this work and others, and indeed within samples are often much larger than their error bars. This suggests there is an intrinsic source of scatter in the observed data, and in this case, one would not expect one model to provide a good fit to the whole sample or even to subsets based on simple flux selection criteria. Here then, we focus on comparing whether one model, or class of models can provide a better description of the data than others, rather than derive the model that describes the data best in an absolute way. We aim to constrain the degeneracies and parameters of our chemical evolution models in a future paper (De Vis et al., in prep.) using a Bayesian approach on a large grid of models. In this section, we will test whether the “eye-ball fits” used here to select “preferred models” (and in similar other studies Zhukovska, 2014; Rémy-Ruyer et al., 2013; Feldmann, 2015) are, in fact, statistically robust.

We do this by calculating a statistic to measure the goodness of fit between the data and each of the models as plotted in Figs. 2, 3, 5. For simplicity, we consider just two data samples: the full sample of all the galaxies together; and the HIGH-low subset. Using all of the galaxies equally weighted together is naturally dominated by the HRS sample which contains the most sources. The HIGH-low subset departs from the typical chemical trends seen in other, more evolved, galaxy samples, and so although we have to contend with small number statistics, this subset represents an unusual and interesting population.

Since our measurements have significant uncertainties in both the $x$ and $y$ values, and our models are non-linear in $y$ as a function of $x$, it is not possible to simply use a standard $\chi^{2}$ approach to determine the goodness of fit. We use a Bayesian approach to determine an effective $\chi^{2}$ which includes the measurement uncertainties in 2 dimensions. We start from Bayes theorem:

[TABLE]

For two measured parameters, $x$ and $y$, an observed data point $d_{i}$ is given by $(x_{i},y_{i})$ with Gaussian uncertainties ($\sigma_{x_{i}},\sigma_{y_{i}}$). If we have a model linking $x$ and $y$, $y=f(x)$, and we know that the true value for $x$ is $x_{0}$, then the true value for $y$ is given by $f(x_{0})$. For this situation, the probability of observing $x_{i}$ and $y_{i}$ is given by:

[TABLE]

Since the magphys uncertainties used in this work are often asymmetric, we have used the lower x-errorbar if $x_{0}<x_{i}$ and the upper errorbar if $x_{0}>x_{i}$, and equivalently for the y-errorbars and $y_{i}$ compared to $f(x_{0})$.

To find the probability of observing the data point given the model $f$, we need to integrate over all possible values for the true value $x_{0}$. Assuming a uniform prior on $x_{0}$, this becomes,

[TABLE]

This integral can be solved numerically since the function $f(x_{0})$ can be described numerically from the output of the chemical evolution models. The normalisation for Equation 7 is found from determining the maximum probability that would be obtained for any y value for the same x-value as the real data point. We thus find the $y_{\text{max}}$ that maximises Equation 7 with respect to $y$, and use this as the normalisation:

[TABLE]

For a linear model, $y_{\text{max}}=f(x_{0})$, so a data point that lies on the model will correspond to $P(d_{i}|f)=1$, which is equivalent to $\chi^{2}=0$, as expected. This normalization reproduces the standard form of $\chi^{2}$ for a linear function with uncertainties in both $x$ and $y$. However, our models are not linear and as a result $y_{\text{max}}$ is not necessarily equal to $f(x_{0})$. By normalising $P(d_{i}|f)$ in this way, we ensure the estimated probability is never larger than 1. This normalisation is thus necessary to obtain sensible results. Formally we should normalise by the maximum of $P(d_{i}|f)$ in both $x$ and $y$, but this 2-d maximisation is time-consuming, and our approximation is valid so long as $\sigma_{x}\frac{d^{2}y}{dx^{2}}<\frac{dy}{dx}$ and $\sigma_{y}\frac{d^{2}x}{dy^{2}}<\frac{dx}{dy}$, which is the case for our models and data.

The likelihood for the entire sample ${\bf d}=\{d_{i}\}$ is then found from taking the product of each of the probabilities for the individual data points from Equation 8. Equivalently, the logarithms of the probabilities can be added to give the log likelihood:

[TABLE]

Additionally, in order to allow for more easily interpretable results, we convert $P(d_{i}|f)$ from Equation 8 to an effective $\chi^{2}$:

[TABLE]

Finally, so we can easily compare samples with different number of galaxies, $n$, we calculate the average contribution to $\chi^{2}_{\text{eff}}$ per data point,

[TABLE]

This statistic is similar to reduced $\chi^{2}$, but does not allow for the number of degrees of freedom; the true number of degrees of freedom is hard to quantify because of correlations between the effects of parameters in our models. Nevertheless, our models are essentially different parameter choices for one over-arching model, and so they all have the same effective degrees of freedom, and the statistic does allow a fair comparison between them.

In this section we revisit three of the main figures in this work and determine how well Models I-VI fit the observations. Table 4 shows the $\langle\chi^{2}_{\text{eff}}\rangle$ for the combined HRS+HAPLESS+HiGH sample and for HiGH-low for Figs. 2, 3 and 5. In these Figs., the models do not reach the lowest gas fractions and highest metallicities. Therefore in our determination of $\langle\chi^{2}_{\text{eff}}\rangle$, we have discarded all data points with $f_{g}<0.2$ for Figs. 2 and 3 and all data points with $12+\log(O/H)>8.5$ for Fig. 5. Figs. 2 and 3 use gas fraction $f_{g}$ on the x-axis. However, the uncertainties on $f_{g}$ become very non-linear near $f_{g}\sim 1$, so the approximation of a Gaussian error probability distribution is not valid, and the calculation of $\langle\chi^{2}_{\text{eff}}\rangle$ is unreliable if we use $f_{g}$ as the $x$ variable. Therefore we have used $M_{g}/M_{*}$ instead. This is entirely equivalent to $f_{g}$ ($M_{g}/M_{*}=f_{g}/(1-f_{g})$), but has errors that are more close to Gaussian, and so our statistic is acceptable. It is worth noting that Models II and V provide a reasonable fit to each of the studied relations and for both sub-samples ($\langle\chi^{2}_{\text{eff}}\rangle<4$). In the rest of this section we will study which of the models fits which relation best and how we interpret these results.

In Table 4 we find that the observations for $M_{d}/M_{\text{bary}}$ versus $M_{g}/M_{*}$ (or equivalently $f_{g}$) are best fitted by Model VI for HiGH-low. We thus find statistical confirmation for our result from Section 5.2 that rapid dust grain growth is necessary to model the unevolved HiGH-low sources. For the combined sample, Model III provides the best fit. Here the HRS dominates the $\langle\chi^{2}_{\text{eff}}\rangle$ because of its large sample size. As previously mentioned in Section 5.2, a reduced dust contribution from LIMS would reduce the $M_{d}/M_{\text{bary}}$ at low $f_{g}$ and thus provide a better match for Models I and II.

The large $\langle\chi^{2}_{\text{eff}}\rangle$ in Table 4 for SFR$/M_{\text{bary}}$ against $M_{g}/M_{*}$ for all models and both sub-samples indicate that a single model cannot describe the spread in SFR$/M_{\text{bary}}$. For both the combined sample and HiGH-low, Model I provides a very poor fit. Delayed SFH thus provide a better description of the overall SFH of normal star-forming galaxies than exponentially declining SFR. For the combined sample, Model III also provides a poor match. The reduction in baryon mass due to the outflows is not matched by reduced star formation in the delayed SFH. We also find Model VI provides a poor match to the combined sample, and surprisingly also to HiGH-low. As can be seen in Fig. 3, HiGH-low includes some very actively star-forming galaxies with small errorbars. These sources are more in line with the bursty sources in DGS and are different from the normal star-forming galaxies we tried to describe by the reduced delayed SFH in Model VI. Therefore, HiGH-low cannot successfully be described by a single Model in Fig. 3.

For $M_{d}/M_{g}$ versus $\rm{12+log(O/H)}$, Table 4 shows Model VI provides the best fit to both HiGH-low and the combined sample. This thus provides further corroboration of dust grain growth being necessary to model the build up of dust as galaxies evolve. For HiGH-low the scatter is rather large, and none of the models provides a particularly good fit.

5.8 Discussion

We have found that, in order to get satisfactory fits to the observed $M_{d}/M_{\rm bary}$, ${\rm SFR}/M_{\rm bary}$, ${\rm 12+log(O/H)}$, $M_{d}/M_{g}$ and $M_{d}/M_{Z}$ for the different nearby galaxy samples in this work, it is necessary to reduce the SNe dust contribution (by a factor of 6-100) and include moderate inflows and outflows, dust destruction and moderate grain growth in our models.

The reverse shock in the remnants of SN likely reduces the produced dust (Bianchi & Schneider, 2007; Gall et al., 2011; De Looze et al., 2016). The Todini & Ferrara (2001) SN dust prescription used in our models does not include a correction for dust destruction by reverse shocks. The need to reduce the SN dust contribution in Models IV-VI could thus, at least in part, be due to dust destruction by the reverse shock.

There is a growing number of studies that suggest significant amounts of dust grain growth are required to model observations in both high and low redshift studies (Dwek et al., 2007; Matsuura et al., 2009; Michałowski et al., 2010; Mattsson & Andersen, 2012; Asano et al., 2013; Grootes et al., 2013; Calura et al., 2014; Rowlands et al., 2014a; Zhukovska, 2014; Nozawa et al., 2015; De Cia et al., 2016). On the other hand, Ferrara et al. (2016) point out the difficulties in obtaining high enough grain growth efficiencies to explain the observations. The subject of dust grain growth thus remains a debated subject. For Models IV-VI, we find grain growth timescales ranging from 1 Gyr - 200 Myrs, similar to those quoted for the Milky Way and local galaxies(e.g. Asano et al., 2013; Mattsson & Andersen, 2012)). There is also evidence for shorter timescales (Draine, 2009; Zhukovska et al., 2008; Feldmann, 2015), which might be more appropriate for the more dust-rich sources at low-metallicity or higher metallicity sources. Variations in the dust growth timescales might also help to explain the differences between dust-rich and dust-poor sources at the same (high) gas fraction. If the reverse shock destroys the majority of the dust grains in SN for all galaxies (and not only the ones modelled well by Model IV-VI), then the higher dust mass sources (which are now fitted by Models I-III) could be explained by shorter dust grain growth timescales, and high dust mass can be reached in spite of a reduced SN contribution. In this scenario, all galaxies have a strongly reduced SN dust contribution compared to Todini & Ferrara (2001), and galaxies with short dust grain growth timescales result in a higher dust content (on the level of Model I), and galaxies with long dust grain growth timescales (such as in Model VI) will have a lower dust content at high gas fractions.

5.9 Caveats

In the previous sections we have used a range of models to explain the dust properties in the dust-poor low-$Z$ sources, as well as dust-rich lower gas fraction sources. In this section we discuss potential caveats of our approach.

• •

Dust Emissivity - If the dust emissivity is different across the samples, this could explain the reduced $M_{d}/M_{\text{H{\sc i}}}$ seen in Fig. 5 and in $M_{d}/M_{Z}$ (Fig. 6). For the dust poor HiGH-low sample to have a dust/metals ratio similar to the HRS and HiGH-high samples (i.e. $\sim 0.4-0.5$), $\kappa$ would have to be $\sim$4 times lower.

• •

Missing molecular gas - We lack sufficient molecular gas information for the HAPLESS and HiGH samples. To affect our results, the molecular mass would have to be larger than the Hi mass. This does not agree with observed molecular gas masses for the HRS and DGS, nor with the galaxy gas-scaling laws from Saintonge et al. (2011) and Bothwell et al. (2014) for a wide range of stellar masses. These results suggest that $M_{\rm H_{2}}/M_{\rm bary}$ is small at all evolutionary phases (see DV17 for more discussion). Using CO derived $\rm H_{2}$ masses for HRS from Boselli et al. (2014), we find that including the molecular gas component does not change the conclusions of our work. At low gas fractions, $M_{\rm H_{2}}/M_{\text{H{\sc i}}}$ is large for some sources, and subsequently these will shift to higher gas fractions and higher total gas masses when molecular gas is included. This shift only results in a better fit to the models at low $f_{g}$ (e.g. Fig. 5).

To study the effects of molecular gas at high gas fraction, we took $M_{\rm H_{2}}$ for DGS from Rémy-Ruyer et al. (2014). These were derived by converting CO fluxes using a constant conversion factor $X_{\mathrm{CO,MW}}$ (Ackermann et al., 2011) or a metallicity-dependent conversion from CO $X_{\mathrm{CO,Z}}$ (Schruba et al., 2012). Using $X_{\mathrm{CO,MW}}$, we again find $M_{\rm H_{2}}/M_{\text{H{\sc i}}}$ is small for all but the lowest gas fractions. The small shift at low gas fractions again results in a better fit with the models. However, if we use $M_{\rm H_{2}}$ derived using $X_{\mathrm{CO,Z}}$, we find significantly higher $M_{\rm H_{2}}/M_{\text{H{\sc i}}}$ at high gas fractions and thus again a shift towards higher gas masses and gas fractions compared to not including molecular gas masses. For the high gas fraction sources this results in a poorer fit to the models assumed here, though the offset does not change our conclusions. Different $X_{\mathrm{CO,Z}}$ factors (see Bolatto et al. 2013 for a review) lead typically to smaller $M_{\rm H_{2}}$ than for $X_{\mathrm{CO,Z}}$ from Schruba et al. (2012), and would thus result in smaller offsets.

• •

Increased dust destruction - We have investigated whether it is possible to explain the observed dust-to-gas properties of the HiGH galaxies by increasing the amount of dust destruction as opposed to reducing the dust production from SNe. We can model increased dust destruction in two ways. Firstly we can increase the amount of dust which is destroyed per SNe (by adjusting the value of $m_{\rm ISM}$ in Eq. 12) and secondly by adjusting the value of $f_{c}$ (the fraction of the ISM in the cold phase). With a larger fraction of the ISM in the warm phase, the efficiency of the dust destruction in the galaxy will be increased. We find that changing dust destruction alone can not match the observed $M_{d}/M_{\rm bary}$ and $M_{d}/M_{g}$ ratios, since an increased dust destruction does not reduce the dust produced at the high gas fractions ($f_{g}>0.8$). Even an extreme model with $m_{\rm ISM}=2500\,\rm{M_{\odot}}$ and $f_{c}=0.01$ would still require significant SNe dust reduction to explain the observed dust-to-gas values. Alternatively, if we change $f_{c}$ to vary with $f_{g}$, this makes at most a factor of 2 difference to dust destruction, whereas we need a reduction of the dust mass by a factor of 10-100 at high $f_{g}$. Therefore the conclusion of needing a reduced dust yield from SNe first put forward by Zhukovska (2014) is robust to changes in the values of $f_{c}$ and $m_{\rm ISM}$. We note that changes in $f_{c}$ and $m_{\rm ISM}$ could reduce the offset in $M_{d}/M_{\rm bary}$ between the observations and some of the models at low gas fractions.

Given our assumption that gas and dust are uniformly mixed, the dust destruction in our model is proportional to the global dust-to-gas ratio. However, in reality $M_{d}/M_{g}$ could be higher in star forming regions than the global average $M_{d}/M_{g}$. More of the SN dust will thus be destroyed before being mixed into the diffuse ISM than is currently the case in our model. Hopkins & Lee (2016) show that on molecular cloud scales, gas-grain decoupling can lead to fluctuations in the local dust-to-gas ratios. For the highest dust-to-gas ratios they predict, dust destruction could remove a significant fraction of the dust (even at high gas fractions) compared to the current model. Conversely, dust grain growth would become more efficient. This could thus provide an alternative interpretation for the scatter in $M_{d}/M_{\rm bary}$ at high gas fractions and the need to reduce the SN dust contributions. A full treatment of this issue requires spatially resolved chemical evolution modeling, which is outside the scope of this work.

• •

Initial mass functions - We have also tested how different IMFs change our results. Changing the model IMF to a more bottom-heavy IMF (e.g. Salpeter, 1955 or Cappellari et al., 2012), reduces the dust and metals produced in the first generation of stars, which results in a better match of these models compared to the observations (i.e. smaller $M_{d}/M_{\rm bary}$ at high gas fractions and smaller $Z$ at low gas fractions). Similarly at high gas fractions, a top-heavy IMF in the model could increase $Z$. But to change the model IMF we must also scale the observational parameters which have been determined using the Chabrier function. For example, using a top-heavy IMF with slope $\alpha=-1.5$ (Cappellari et al., 2012; Madau & Dickinson, 2014) we would have to scale the stellar mass and SFRs by a factor of 0.32 (Michałowski, 2015). This results in models that are nearly indistinguishable (in terms of a ‘good-fit’) compared to the scatter in the relations.

6 Conclusions

In this paper, we have brought together the Hi-selected HiGH, dust-selected HAPLESS, stellar mass selected HRS and the metallicity-selected DGS sources. Compared to the 126 sources from Rémy-Ruyer et al. (2014), we have increased the sample size to 382 sources (including 48 DGS sources in both samples). Beyond the 37 DGS sources with $Z<1/5\,Z_{\odot}$, we have added a further 67 sources with a metallicity smaller than $1/3\,Z_{\odot}$, including 15 sources below $1/5\,Z_{\odot}$. Following Zhukovska (2014) and Feldmann (2015), we have investigated the dust trends of these samples using a chemical evolution model (an updated version of Rowlands et al., 2014b and Morgan & Edmunds, 2003). We use the PG16S metallicity calibration, which was found to be the most reliable calibration for the low metallicity sources, and gas fraction (a proxy for the evolutionary state) to track and constrain the build-up of dust and metals as gas is converted into stars, from very high ($f_{g}=0.97$) to very low ($f_{g}=0.05$) gas fractions. We find that:

• •

DGS sources are selected to have low metallicities, which leads to a selection of very actively star forming galaxies. For a given gas fraction or stellar mass, we have found our low $M_{*}$ HiGH and HAPLESS samples to be more normal in terms of star formation properties and metallicity. These samples thus complement the DGS, and provide additional, new information on more normal star-forming galaxies in the nearby Universe.

• •

Delayed star formation history models are necessary to match the evolution of ${\rm SFR}/M_{\rm bary}$ for our normal star-forming galaxies.

• •

We find that low–moderate metallicity galaxies (a) can have dust properties that are consistent with dust production at early stages being dominated by SNe dust (as in C15), and thus with a linear $M_{d}/M_{g}-Z$ relationship and constant $M_{d}/M_{Z}$; or (b) have dust masses well below these trends, with a much smaller contribution from SNe dust. The lowest metallicity sources fall in the latter category and to model them we require a maximum of $0.01-0.16\,\rm M_{\odot}$ of dust per core collapse SN.

• •

The dust properties and the observed correlation of $M_{d}/M_{Z}-Z$ for low metallicity sources are well matched when including: (i) reduced stardust contribution by 6-100, particularly from core-collapse SNe as the reduced dust component has to act at very high gas fractions. (ii) Moderate ($2.5\times$ SFR) enriched outflows and metal-poor inflows to keep the model metallicity from rising to higher than observed metallicties at low gas fractions. (iii) Dust destruction and moderate grain growth (timescales ranging from 1 Gyr - 200 Myrs, similar to those quoted for the MW and local galaxies; Draine 2009; Asano et al. 2013; Mattsson & Andersen 2012; Mattsson et al. 2014). The need for this moderate grain growth is corroborated by the good statistical match of Model VI to the $M_{d}/M_{g}-Z$ relation for both HiGH-low and the combined HRS+HAPLESS+HiGH sample.

• •

As we show that neither the dust-to-metals nor the dust-to-gas ratio are constant during the evolution of a galaxy, we urge caution when using dust as a tracer of gas mass in galaxies (e.g. Eales et al., 2010; Scoville et al., 2014). Assuming a universal value for either is unwise and unlikely to produce reliable results, particularly for low stellar mass systems.

• •

In our best models, we find that grain growth produces, by mass, 70-93 per cent of the total dust created over the lifetime of these galaxies, and the metallicity at which dust grain growth exceeds stellar dust sources in our model is reached between $7.97<12+\rm{log(O/H)}<8.63$ (or $0.88>f_{g}>0.53$).

We show our Model VI (SN dust contribution reduced by factor 100, inflows and outflows of $2.5\times$ SFR, delayed SFH, dust grain growth and destruction) is consistent with all of the observed properties (except the SFR for some rather bursty sources) of the HiGH-low galaxies, the first normal star forming population of low stellar mass galaxies studied in this way. Comparing the data and models using a Bayesian approach confirms that Model VI provides the best statistical match to the HiGH-low data for the dust-to-baryon ratio against gas fraction and the dust-to-gas ratio against metallicity. For $\rm{SFR/M_{bary}}$, it is not possible to find one model that describes all the HiGH-low data since the intrinsic scatter within the sample is larger than the errorbars. When Model VI is combined with a bursty SFH (as shown originally in Zhukovska 2014) and stronger outflows (Feldmann, 2015, Model VII), this scenario is also consistent with the DGS galaxies at similar $f_{g}$, $M_{*}$ and $Z$ without requiring extremely rapid grain growth timescales and extreme outflows for low metallicity galaxies.

Acknowledgments

We gratefully acknowledge the anonymous referee for useful suggestions. We thank Suzanne Madden for providing the emission line tables for the Dwarf Galaxy Survey. PDV acknowledges funding from the Fund for Scientific Research Flanders. SPS acknowledges funding from the Science and Technology Facilities Council (STFC) Doctoral Training Grant scheme. HLG acknowledge support from the STFC Consolidated Grant scheme (ST/K000926/1). PDV, HLG, LD, PC and SJM acknowledge support from the European Research Council (ERC) in the form of Consolidator Grant CosmicDust. LD and SJM acknowledge support from the Advanced Investigator programme COSMICISM. GAMA is a joint European-Australasian project based around a spectroscopic campaign using the Anglo-Australian Telescope. The GAMA input catalogue is based on data taken from the Sloan Digital Sky Survey and the UKIRT Infrared Deep Sky Survey. Complementary imaging of the GAMA regions is being obtained by a number of independent survey programmes including GALEX MIS, VST KiDS, VISTA VIKING, WISE, Herschel-ATLAS, GMRT and ASKAP providing UV to radio coverage. GAMA is funded by the STFC (UK), the ARC (Australia), the AAO, and the participating institutions. The GAMA website is http://www.gama-survey.org/ . This research has made use of GitHub, Astropy 21 (Astropy Collaboration et al., 2013), NumPy 24 (Walt et al., 2011), SciPy 25, and MatPlotLib 26 (Hunter, 2007).

Appendix A Emission lines and galaxy properties

The basic galaxy properties for the HiGH and HAPLESS galaxies are listed in Table 5 and their metallicities in Table 6. Star formation rates and stellar and dust masses were derived using magphys (See DV17 for details). Metallicities were derived for 4 different calibrations, using a weighted average of the metallicities from individual Hii regions within the galaxy (Section 3). The emission lines for each Hii region in the HiGH and HAPLESS galaxies are listed in Table 7. For DGS, emission lines from literature and derived metallicities are provided in Table 8.

Appendix B Dust mass equation

Here we reproduce the equation for the dust mass evolution, $M_{d}$, for the chemical evolution model used in this work:

[TABLE]

$M_{g}$ is the gas mass, $\psi(t)$ is the star formation rate, $\phi(m)$ is the stellar IMF, $Z$ is the metal mass fraction defined as $M_{Z}/M_{g}$ and $m_{R}$ is the remnant mass of a star of mass $m$ (Ferreras & Silk, 2000). The first term accounts for dust formed in stars and supernovae. This includes metals re-released by stars after they die, and newly synthesised metals ejected in winds and supernovae. The second term describes the removal of dust due to astration and the grain destruction and growth timescales are given in terms three and four. The fifth term allows us to include primordial dust in the galaxy for example associated with Pop III stars, we set this to zero (Rowlands et al., 2014b). Finally, $I(t)$ and $O(t)$ are simple parameterisations of dust removed or contributed via inflows and outflows. The lifetime $\tau_{m}$ of stars with initial mass $m$ is derived using the model in Schaller et al. (1992) and yields for LIMS and massive stars are taken from van den Hoek & Groenewegen (1997) and Maeder (1992) respectively. A full discussion on the effect of using different yields can be seen in Rowlands et al. (2014b), and Romano et al. (2010).

Appendix C Comparison of models with Dwarf Galaxy Survey

We can model the properties of the DGS sources by including strong inflows and outflows (Feldmann, 2015) and a bursty SFH (Zhukovska, 2014) in the chemical evolution (Model VII). The results are shown in Fig. 8 using the original DGS metallicities (transparent triangles), and the revised PG16S metallicities derived in this work. In the top-left panel, we compare the $M_{d}/M_{\rm bary}$ of the DGS with Model VII (as we did with the HRS, HiGH and HAPLESS in Fig. 2). Model VII matches the observed trend well. In the top-right panel of Fig. 8, we compare the predicted ${\rm SFR}/M_{\rm bary}$ with gas fraction for Model VII. Here we see that the bursty model is required to explain the elevated ${\rm SFR}/M_{\rm bary}$ of the DGS galaxies compared to the HAPLESS, HRS and HiGH samples. In the bottom-left panel, we find the observed metallicities for DGS tend to be lower than for the other samples and are well matched by Model VII, due to including strong inflows and outflows at a rate of 4 times the SFR.