Atacama Cosmology Telescope: Dusty star-forming galaxies and active galactic nuclei in the equatorial survey

TL;DR

This paper presents a comprehensive catalog of millimeter-wave active galactic nuclei and dusty star-forming galaxies from the Atacama Cosmology Telescope, including new techniques for source detection, contamination removal, and flux debiasing, with detailed spectral and count analyses.

Contribution

The study introduces the most sensitive wide-field millimeter-wave DSFG selection to date, along with novel methods for Galactic contamination removal and flux debiasing in heterogeneous samples.

Findings

Detected 510 AGN and 287 DSFGs with >5σ significance.

First source counts measurement at 277 GHz for these flux densities.

DSFG spectra suggest optically thick emission or cold dust components.

Abstract

We present a catalog of 510 radio-loud active galactic nuclei (AGN, primarily blazars) and 287 dusty star-forming galaxies (DSFGs) detected by the Atacama Cosmology Telescope at $>5\sigma$ significance in bands centered on 148 GHz (2 mm), 218 GHz (1.4 mm) and 277 GHz (1.1 mm), from a 480 square degrees strip on the celestial equator with additional (360 square degrees) shallower fields. Combining the deepest available 218 GHz wide-field imaging, 277 GHz data, and multi-band filtering yields the most sensitive wide-field millimeter-wave DSFG selection to date with rms noise referenced to 218 GHz reaching $<2$ mJy. We developed techniques to remove Galactic contamination from the extragalactic catalog, yielding 321 additional Galactic sources. We employ a new flux debiasing method that handles the heterogeneous sample selection due to Galactic cuts. We present spectral properties and source counts of the AGN and DSFGs. The DSFG spectra depart from an optically thin modified blackbody between 218 GHz and 277 GHz, consistent with optically thick emission or an additional cold dust component. For bright AGN, the inter-year RMS fractional deviation in flux density from source variability is $\sim40\%$. We report 8$-$2870 mJy source counts for AGN and 8$-$90 mJy source counts for DSFGs, the latter probing both the brighter, lensed population and the fainter, unlensed population. At 277 GHz we report the first source counts measurements at these flux densities, finding an excess above most model count predictions. Finally, we select thirty of the brightest DSFGs for multi-frequency study as candidate high-$z$ lensed systems.

Tables

Table 1. Table 1: A Sample of the Extragalactic Catalog 7 7 7 The entries in this catalog sample correspond to the sources found in the data shown in Figure 3 . Only a subset of columns in the full catalog are shown.

ACT-S ID888The ACT-S ID encodes the sexagesimal position of each source (hhmmss$\pm$ddmmss).	S/N	Selection999The selection dataset is that in which the source is detected with the highest S/N (listed at left).	${\alpha }_{148}^{218}$ 101010For inter-band spectral indices (${\alpha }_X^Y$) and flux densities ($S_X$), raw (debiased) values are given outside (inside) parentheses.	${\alpha }_{148}^{277}$/${\alpha }_{218}^{277}$ 111111For 148 GHz-selected sources, we report $\alpha$ between 148 GHz and 277 GHz, whereas for MMF and 218 GHz selection, we report $\alpha$ between 218 GHz and 277 GHz.	Type121212The type of source is designated as an AGN (DSFG) if the 148$-$218 GHz spectral index is less than (greater than) unity as described in Section 5.1.	$S_{148}$	$S_{218}$	$S_{277}$
(J2000)						(mJy)	(mJy)	(mJy)
003337$+$000353	6.4	MMF	$3.2$	$3.6$	DSFG	3.5$\pm$1.8	12.2$\pm$2.8	28.7$\pm$5.4
			(${3.0}_{-1.4}^{+1.6}$)	(${3.5}_{-1.3}^{+1.4}$)		(2.7${}_{-1.3}{}^{}{}_{}{}^{+1.6}$)	(8.8${}_{-3.2}{}^{}{}_{}{}^{+3.7}$)	(22.7${}_{-5.2}{}^{}{}_{}{}^{+5.3}$)
003626$+$001301	6.4	148	$0.2$	$-0.6$	AGN	11.9$\pm$1.9	12.9$\pm$2.8	8.3$\pm$5.7
			(${0.1}_{-0.7}^{+0.7}$)	($-{1.1}_{-1.9}^{+1.2}$)		(10.7${}_{-2.2}{}^{}{}_{}{}^{+2.2}$)	(11.4${}_{-2.5}{}^{}{}_{}{}^{+2.6}$)	(5.2${}_{-3.6}{}^{}{}_{}{}^{+5.6}$)
003648$-$002052	5.9	MMF	$4.1$	$3.5$	DSFG	2.3$\pm$1.8	11.7$\pm$2.8	26.6$\pm$5.6
			(${3.5}_{-1.5}^{+1.7}$)	(${3.3}_{-1.4}^{+1.5}$)		(2.0${}_{-1.0}{}^{}{}_{}{}^{+1.4}$)	(8.4${}_{-3.0}{}^{}{}_{}{}^{+3.6}$)	(20.3${}_{-5.2}{}^{}{}_{}{}^{+5.3}$)
003808$+$001334	20.3	148	$-0.3$	$-0.7$	AGN	37.4$\pm$1.8	33.3$\pm$2.7	23.4$\pm$5.8
			($-{0.3}_{-0.3}^{+0.3}$)	($-{0.8}_{-0.5}^{+0.4}$)		(37.1${}_{-2.5}{}^{}{}_{}{}^{+2.5}$)	(32.9${}_{-2.7}{}^{}{}_{}{}^{+2.7}$)	(21.8${}_{-6.0}{}^{}{}_{}{}^{+6.0}$)
003814$-$002255	10.7	MMF	$3.0$	$1.9$	DSFG	7.6$\pm$1.8	24.6$\pm$2.7	39.1$\pm$5.5
			(${3.0}_{-0.7}^{+0.8}$)	(${2.0}_{-0.8}^{+0.9}$)		(7.1${}_{-1.7}{}^{}{}_{}{}^{+1.8}$)	(23.5${}_{-3.8}{}^{}{}_{}{}^{+2.7}$)	(36.9${}_{-5.3}{}^{}{}_{}{}^{+5.3}$)
003826$-$000044	6.0	MMF	$4.4$	$1.0$	DSFG	2.9$\pm$1.8	15.7$\pm$2.7	20.2$\pm$5.5
			(${3.7}_{-1.4}^{+1.5}$)	(${1.7}_{-1.5}^{+1.6}$)		(2.6${}_{-1.2}{}^{}{}_{}{}^{+1.5}$)	(11.8${}_{-4.7}{}^{}{}_{}{}^{+3.9}$)	(15.3${}_{-4.6}{}^{}{}_{}{}^{+4.8}$)
003929$+$002422	8.9	MMF	$3.2$	$2.1$	DSFG	5.7$\pm$1.8	20.0$\pm$2.7	33.4$\pm$5.5
			(${3.1}_{-1.0}^{+1.1}$)	(${2.2}_{-1.0}^{+1.1}$)		(5.1${}_{-1.6}{}^{}{}_{}{}^{+1.8}$)	(17.7${}_{-4.5}{}^{}{}_{}{}^{+3.9}$)	(29.9${}_{-5.2}{}^{}{}_{}{}^{+5.3}$)
003943$-$003952	6.6	MMF	$4.4$	$4.0$	DSFG	2.2$\pm$1.8	12.1$\pm$2.7	31.8$\pm$5.5
			(${3.6}_{-1.5}^{+1.7}$)	(${3.9}_{-1.2}^{+1.4}$)		(2.0${}_{-1.0}{}^{}{}_{}{}^{+1.4}$)	(8.6${}_{-3.1}{}^{}{}_{}{}^{+3.7}$)	(26.2${}_{-5.3}{}^{}{}_{}{}^{+5.4}$)
004020$-$004035	37.1	148	$-0.8$	$-0.5$	AGN	68.5$\pm$1.8	49.4$\pm$2.8	51.2$\pm$5.8
			($-$)131313Debiasing is not computed for 148 GHz-selected sources with $S_{148}>50$ mJy. Therefore debiased spectral indices are not provided. For ease of catalog use, raw flux densities are reported in the debiased flux density columns. For this class of bright source, the raw and debiased estimates of flux densities are equivalent.	($-$)		(68.5${}_{-1.8}{}^{}{}_{}{}^{+1.8}$)	(49.4${}_{-2.8}{}^{}{}_{}{}^{+2.8}$)	(51.2${}_{-5.8}{}^{}{}_{}{}^{+5.8}$)
004033$+$000228	5.6	MMF	$2.7$	$2.7$	DSFG	4.1$\pm$1.8	11.7$\pm$2.7	22.0$\pm$5.5
			(${2.6}_{-1.3}^{+1.5}$)	(${2.6}_{-1.5}^{+1.5}$)		(3.1${}_{-1.5}{}^{}{}_{}{}^{+1.7}$)	(8.4${}_{-3.0}{}^{}{}_{}{}^{+3.6}$)	(15.7${}_{-4.7}{}^{}{}_{}{}^{+5.0}$)
004332$+$002456	24.5	148	$-0.8$	$-0.3$	AGN	45.1$\pm$1.8	32.8$\pm$2.7	37.6$\pm$5.8
			($-{0.8}_{-0.3}^{+0.3}$)	($-{0.3}_{-0.3}^{+0.3}$)		(44.7${}_{-2.5}{}^{}{}_{}{}^{+2.6}$)	(32.6${}_{-2.7}{}^{}{}_{}{}^{+2.7}$)	(36.6${}_{-5.7}{}^{}{}_{}{}^{+5.8}$)
004454$+$002509	5.8	MMF	$4.7$	$4.4$	DSFG	1.6$\pm$1.8	10.1$\pm$2.7	28.7$\pm$5.5
			(${3.8}_{-1.5}^{+1.7}$)	(${3.7}_{-1.3}^{+1.4}$)		(1.7${}_{-0.9}{}^{}{}_{}{}^{+1.2}$)	(7.8${}_{-2.6}{}^{}{}_{}{}^{+3.4}$)	(22.3${}_{-5.2}{}^{}{}_{}{}^{+5.4}$)
004532$-$000127	11.1	MMF	$3.8$	$2.1$	DSFG	5.9$\pm$1.8	25.9$\pm$2.8	42.4$\pm$5.6
			(${3.8}_{-0.8}^{+0.9}$)	(${2.1}_{-0.8}^{+0.9}$)		(5.6${}_{-1.7}{}^{}{}_{}{}^{+1.7}$)	(25.0${}_{-4.3}{}^{}{}_{}{}^{+3.5}$)	(40.7${}_{-5.3}{}^{}{}_{}{}^{+5.4}$)
004624$-$003424	6.4	MMF	$4.7$	$2.1$	DSFG	2.4$\pm$1.8	15.2$\pm$2.8	25.1$\pm$5.5
			(${3.8}_{-1.4}^{+1.6}$)	(${2.4}_{-1.4}^{+1.5}$)		(2.2${}_{-1.1}{}^{}{}_{}{}^{+1.5}$)	(11.1${}_{-4.6}{}^{}{}_{}{}^{+4.0}$)	(19.7${}_{-5.0}{}^{}{}_{}{}^{+5.1}$)
004810$+$002750	6.3	MMF	$1.8$	$2.3$	DSFG	6.7$\pm$1.8	13.3$\pm$2.8	23.1$\pm$5.5
			(${1.6}_{-1.1}^{+1.1}$)	(${2.5}_{-1.5}^{+1.5}$)		(5.7${}_{-1.9}{}^{}{}_{}{}^{+1.8}$)	(9.5${}_{-3.9}{}^{}{}_{}{}^{+3.9}$)	(17.2${}_{-4.9}{}^{}{}_{}{}^{+5.1}$)
004818$+$001452	13.7	148	$-1.1$	$-2.1$	AGN	25.4$\pm$1.9	16.9$\pm$2.8	6.6$\pm$5.8
			($-{1.0}_{-0.5}^{+0.4}$)	($-{2.4}_{-1.4}^{+1.0}$)		(24.6${}_{-2.6}{}^{}{}_{}{}^{+2.6}$)	(16.7${}_{-2.5}{}^{}{}_{}{}^{+2.6}$)	(5.5${}_{-3.3}{}^{}{}_{}{}^{+5.0}$)

Table 2. Table 2: ACT Equatorial Catalog Summary

	Extragalactic	Galactic
	Catalog161616The full source sample is divided into extragalactic and Galactic catalogs based on cuts for Galactic contamination (Section 4.2).	Catalog
Total Detections	797	321
148 GHz Selection171717Selection methods correspond to the map in which a source has the highest significance (Section 4).	504	13
MMF Selection	217	82
218 GHz Selection	76	226
In Auxiliary Fields181818Auxiliary fields, with only 148 GHz data, are shown in Figure 2.	112	N/A
DSFG191919Distinction between DSFG and AGN is based on spectral information (Section 5.1) whereas DSFG* and AGN* denote sources lacking spectral information but sorted into source category based on the detection map (most are from auxiliary fields). Typical source spectral indices are listed in Table 4.	268	N/A
AGN	376	N/A
DSFG*	19	N/A
AGN*	134	N/A
CO202020CO indicates a spectrum in sources flagged as Galactic that is indicative of CO(2–1) emission (Section 4.2).	N/A	178
Nearby Galaxy/Star212121This flag denotes sources that lie within 50′′ of a nearby () galaxy in SDSS or by one of three radio/far-infrared bright stars.	68	12

Table 3. Table 3: Astrometry

	$N_{\mathrm{match}}$232323ACT locations are compared to matched FIRST source locations, which have ${0.5}^{\prime \prime }$ precision.	R.A.242424Offsets listed in arcsec.		decl.
		mean	$\sigma$	mean	$\sigma$
148 GHz, S/N $>$ 5	264	$-0.02$	5	$-0.8$	5
148 GHz, S/N $>$ 16	94	$-0.2$	2	0.1	2
MMF, S/N $>$ 5	151	$-0.1$	4	$-2$	5
MMF, $S/N>16$	47	$-0.1$	2	$-0.8$	2

Table 4. Table 4: Typical millimeter spectral indices for AGN and DSFGs

Median spectral indices	AGN				DSFGs
	median ${\alpha }_{148}^{218}$	$\sigma$	median ${\alpha }_{148}^{277}$	$\sigma$	median ${\alpha }_{148}^{218}$	$\sigma$	median ${\alpha }_{218}^{277}$	$\sigma$
Full sample252525The spectral indices reported here are based on raw (not debiased) flux densities, except where otherwise noted.	$-0.66$	1.2	$-0.54$	1.9	3.7	1.8	2.4	4.2
Restricted to	$-0.65$	0.9	$-0.45$	1.1	3.6	1.6	2.7	1.5
Restricted to $S>20$ mJy AGN	$-0.62$	0.67	$-0.50$	1.0
Restricted to $S>50$ mJy AGN	$-0.58$	0.32	$-0.49$	1.3
Bootstrap samples262626For this row, $\sigma$ denotes the standard deviation of the medians, while for the other rows $\sigma$ denotes the sample standard deviation. The statistics reported here are based on resampling the full catalog 1,000 times.	$-0.66$	0.03	$-0.54$	0.04	3.7	0.13	2.4	0.11
Debiased spectral indices272727The debiased spectral index for each source is calculated from the posterior distribution of the spectral index, as discussed in Section 4.3. For ${\alpha }_{148}^{277}$ and ${\alpha }_{218}^{277}$, the sources are restricted to dec .	$-0.52$	0.6	$-0.67$	2.6	3.8	1.1	2.8	1.1
Parameters describing best-fit Gaussian	$-0.8$	0.4			$3.4$	1.3
Prior used in debiasing secondary bands	$-0.7$	1.2	$-0.4$	3.4	3.7	2.2	2.7	2.1
Nearby dusty galaxies					4.0	1.7	1.3	4.2

Equations

$$T_{\mathrm{MF}}(\mathbf{\theta })=\int \exp (2\pi i\mathbf{k}\cdot \mathbf{\theta }){\Phi }_{\mathrm{MF}}(\mathbf{k})\tilde{T}(\mathbf{k})d\mathbf{k},$$

$${\Phi }_{\mathrm{MF}}(\mathbf{k})=\frac{F_{k_0,k_x}(\mathbf{k}){\tilde{B}}^{\ast }(\mathbf{k})|{\tilde{T}}_{\mathrm{other}}(\mathbf{k}){|}^{-2}}{\int {\tilde{B}}^{\ast }({\mathbf{k}}^{\prime })F_{k_0,k_x}({\mathbf{k}}^{\prime })|{\tilde{T}}_{\mathrm{other}}({\mathbf{k}}^{\prime }){|}^{-2}\tilde{B}({\mathbf{k}}^{\prime })d{\mathbf{k}}^{\prime }}$$

$$S_{\mathrm{MF}}=W^{-1}(\mathbf{\theta })\frac{\partial I_{BB}}{\partial T}{T}_{\mathrm{MF}}(\mathbf{\theta }){\Omega }_B,$$

$$T_{\mathrm{MMF}}(\mathbf{\theta })=\int \exp (2\pi i\mathbf{k}\cdot \mathbf{\theta })\sum _{\nu }{\Psi }_{\nu }(\mathbf{k})T_{\nu }(\mathbf{k})d\mathbf{k},$$

$$S_{\mathrm{MMF},{\nu }_0}(\mathbf{\theta })=\sum _{\nu }{w}_{\nu }(\mathbf{\theta })S_{\mathrm{MF};\nu }(\mathbf{\theta }),$$

$$w_{\nu }(\mathbf{\theta })={\sigma }_{\mathrm{MMF}}^2(\mathbf{\theta })\frac{f_{\nu }}{{\sigma }_{\nu }^2(\mathbf{\theta })}.$$

$$f_{\nu }=\frac{S_{\nu }}{S_{{\nu }_0}}={\left(\frac{\nu }{{\nu }_0}\right)}^{\alpha }.$$

$${\sigma }_{MMF}^2(\mathbf{\theta })={\left(\sum _{\nu }\frac{f_{\nu }^2}{{\sigma }_{\nu }^2(\mathbf{\theta })}\right)}^{-1}.$$

$$P(S_1,\alpha$$

$$\approx P(S_1^m\mid S_1)P(S_2^m\mid S_1,\alpha )P(\alpha \mid S_1)P(S_1)$$

$$\propto P(S_2^m\mid S_1,\alpha )P(\alpha \mid S_1)P(S_1\mid S_1^m).$$

Full text

catalogs — surveys — galaxies: active — galaxies: starburst

1 Introduction

Wide-field millimeter-wave surveys open a unique window on the extragalactic universe beyond their traditional association with the primordial cosmic microwave background (CMB). In particular, galaxies are detected in these surveys through their millimeter-wave emission. Strong extragalactic sources of millimeter emission fall into two categories. The first source category is characterized by self-absorbing synchrotron radiation extending from radio to millimeter wavelengths. In these sources, jets from active galactic nuclei (AGN) impart relativistic velocities to electrons that in turn generate synchrotron radiation in the galaxy’s magnetic field. Self-absorption of synchrotron radiation is observed when the optically thick emission core of the AGN is within the observer’s line of sight. These sources are categorized observationally as blazars, BL Lacertae objects, or flat spectrum radio quasars.111For the purposes of this paper we will refer to all these synchrotron-source classifications collectively as “AGN” or “blazars”. Measurements of their synchrotron spectra provide a unique perspective on AGN jets (e.g., Blandford & Königl 1979; Toffolatti et al. 1998; de Zotti et al. 2005; Tucci et al. 2011). The second source category is characterized by thermal radiation from dust extending from millimeter to far-infrared wavelengths. The dust is heated by UV and optical emission, notably from massive young stars in these dusty star-forming galaxies (DSFG). Since the first studies of DSFGs as sub-millimeter galaxies at 850 $\mu$m (SMGs; e.g., Smail et al. 1997; Hughes et al. 1998; Barger et al. 1998), we have learned that the most prodigious star-formation in the universe generates and is enshrouded by significant dust, making DSFGs important in the history of cosmic star-formation (e.g., Lilly et al. 1996; Madau et al. 1996; Blain et al. 2002; Chapman et al. 2005; Le Floc’h et al. 2005; Pérez-González et al. 2005; Hopkins & Beacom 2006; Daddi et al. 2007; Elbaz et al. 2007; Casey et al. 2014; Madau & Dickinson 2014).

Current state-of-the-art wide-field ($>100$ sq-deg) millimeter-wave source surveys have been conducted by three observatories: the Atacama Cosmology Telescope (ACT; Marriage et al. 2011; Marsden et al. 2014), the Planck Satellite (Planck Collaboration et al. 2011c, 2014, 2016), and the South Pole Telescope (SPT; Vieira et al. 2010; Mocanu et al. 2013). At longer radio wavelengths, surveys such as the Very Large Array (VLA) Faint Images of the Radio Sky at Twenty centimeters (FIRST) survey (Becker et al. 1995) and the Australia Telescope 20 GHz Survey (Murphy et al. 2010) provide important complementary data on the millimeter-bright AGN population. At shorter sub-millimeter/far-infrared wavelengths, the Herschel Space Observatory has undertaken the most comprehensive wide-field source surveys probing the DSFG population (e.g., Oliver et al. 2012; Viero et al. 2014; Valiante et al. 2016) with additional contributions from the Submillimetre Common-User Bolometer Array 2 (Geach et al. 2017; Holland et al. 2013). Complementing these 100+ deg2 surveys, there have been a host of smaller, deeper surveys by AzTEC at the JCMT and ASTE (e.g., Austermann et al. 2009, 2010), by the Max-Planck Millimeter Bolometer Array (MAMBO) and the Goddard-IRAM Superconducting 2-mm Observer (GISMO) on the IRAM 30 m telescope (e.g., Bertoldi et al. 2007; Lindner et al. 2011; Staguhn et al. 2014), by Bolocam at the CSO (e.g., Laurent et al. 2005), and the Large APEX BOlometer CAmera (LABOCA) on the APEX telescope (e.g., Greve et al. 2010).

The wide-field millimeter-wave surveys have modified our understanding of the blazar population. The first catalogs from ACT, Planck, and SPT provided unprecedented source count data at $150$ GHz (2 mm) and $220$ GHz (1.4 mm), spanning more than three orders of magnitude in flux density down to 10 mJy (Vieira et al. 2010; Marriage et al. 2011; Planck Collaboration et al. 2011a). To fit the new millimeter-wave data, Tucci et al. (2011) and others have introduced new models that inform classical models of blazar jets. Since then, expanded millimeter-wave catalogs have further constrained these new models (Mocanu et al. 2013; Marsden et al. 2014; Planck Collaboration et al. 2014, 2016; Datta et al. 2019).

The second millimeter-bright source population is comprised of DSFGs. A subset of the strictly millimeter-selected DSFGs are local star-forming galaxies, which are bright in optical and infrared catalogs (e.g., Planck Collaboration et al. 2011b). However, the majority of the DSFGs detected in the millimeter-wave surveys to date correspond to lensed, high-redshift DSFGs (e.g., Negrello et al. 2007). The first detections at 1.4 mm were announced by SPT (Vieira et al. 2010), and subsequent work by ACT, Planck, and SPT have expanded the number of published millimeter-selected candidates to many hundreds (Mocanu et al. 2013; Marsden et al. 2014; Cañameras et al. 2015). As with classical SMGs, the UV and optical light from these galaxies is heavily obscured, leaving nearly all information about the sources in the far-infrared thermal dust spectra and accompanying molecular line spectra. Extensive complementary observations and modeling have established that the millimeter-selected DSFG population is magnified via gravitational lensing by typical factors of $5-10$ with redshifts $z=2-6$, dust temperatures $T=30-60$ K, and significant dust optical depth at the peak of the thermal spectrum ($\lambda\approx 100$ $\mu$m in the rest frame) (e.g., Greve et al. 2012; Hezaveh et al. 2013; Vieira et al. 2013; Weiß et al. 2013; Cañameras et al. 2015; Harrington et al. 2016; Strandet et al. 2016; Spilker et al. 2016; Su et al. 2017). In addition to the millimeter-wave surveys, far-infrared surveys conducted by Herschel have provided extensive samples of DSFGs that are being similarly characterized and studied (e.g.; Negrello et al. 2010; Bussmann et al. 2013; Wardlow et al. 2013).

This work is part of a series of publications of millimeter-wave source catalogs from ACT. Marriage et al. (2011) and Marsden et al. (2014) provided catalogs of AGN and DSFGs in the ACT southern survey centered at declination $-52\mathrm{\SIUnitSymbolDegree}$. Recently Datta et al. (2019) published the first polarized source study from the ACTPol survey. Here we describe the detection and initial characterization of sources in the ACT equatorial survey (decl. $0\mathrm{\SIUnitSymbolDegree}$, range $\pm 2.2\mathrm{\SIUnitSymbolDegree})$. New to our approach is the addition of the ACT 277 GHz data together with a multi-frequency matched filter (MMF) to optimize DSFG detection across all three ACT frequency bands. Additionally, the presence of dust and CO emission from the Milky Way forces the introduction of systematic cuts for Galactic contamination. To handle the new source selection methods, we employ a new flux density debiasing technique described in Gralla & Marriage (submitted) to account for Eddington bias, which is an important consideration for the faintest DSFGs. Enabled by the extra high-frequency channel and MMF, our sensitivity to DSFGs reaches a new level for a wide-field millimeter-wave survey. The rms equivalent 218 GHz standard error is $2-3$ mJy, compared to $3-4$ mJy in Mocanu et al. (2013) and Marsden et al. (2014). At this survey depth, a significant fraction of the recovered DSFGs are predicted to be unlensed high-$z$ systems, similar to those probed by Herschel (e.g., Magnelli et al. 2012; Asboth et al. 2016; Nayyeri et al. 2016).

This paper is organized as follows. Section 2 introduces the ACT equatorial survey. How we processed the data to produce catalogs is presented in Section 3, with the catalogs themselves presented in Section 4. The sources are characterized on the basis of their spectral properties, counts, and variability in Section 5, and a sub-sample of the brightest DSFGs that we have chosen for targeted follow-up is presented in Section 6. We conclude in Section 7. Throughout this work, $\alpha$ denotes the spectral index, relating flux density ($S$) to frequency ($\nu$) according to $S(\nu)\propto\nu^{\alpha}$. In our bands, typical values are $\alpha=-0.7$ for synchrotron emission and $\alpha=3.5$ for dust emission.

2 Data

ACT is a 6 m telescope located in the Atacama Desert at an elevation of 5,190 m (Fowler et al. 2007). ACT’s first receiver was the Millimeter Bolometric Array Camera (MBAC; Swetz et al. 2011). Using the MBAC, ACT conducted surveys of the southern ($\delta=-52\mathrm{\SIUnitSymbolDegree}12\mathrm{\SIUnitSymbolArcminute}$) and equatorial sky from 2008 to 2010. For these, ACT observed at three frequencies simultaneously: 148 GHz (2.0 mm), 218 GHz (1.4 mm) and 277 GHz (1.1 mm) with angular resolutions of 1.4*′, 1.0′* and 0.9*′*, respectively (Hasselfield et al. 2013b). The telescope has since been upgraded with two successive generations of polarization sensitive receivers: ACTPol, described in Thornton et al. 2016, and Advanced ACTPol, outlined in Henderson et al. 2016. (MBAC was not polarization sensitive.) This paper presents point source catalogs from the MBAC-based equatorial survey. The 148 and 218 GHz data are from the 2009 and 2010 observing seasons, and the 277 GHz data are from the 2010 observing season.

Figure 1 shows the main ACT equatorial survey region used in this study. This region covers approximately 480 $\deg^{2}$ on the celestial equator with R.A. centered at 0h and spanning from 19h45m to 4h16m, and with declination ranging from $-2^{\circ}12\mathrm{\SIUnitSymbolArcminute}$ to $2^{\circ}12\mathrm{\SIUnitSymbolArcminute}$. There are, however, notable differences in survey coverage between bands. The survey region for the 277 GHz band, derived from only one year of observations, is smaller than that of the lower frequency bands. For the 148 GHz band, sources were identified in additional equatorial regions centered on R.A. 8h and 16h (Figure 2). These additional regions increase the survey area for 148 GHz by 360 $\deg^{2}$. Each map is produced with a cylindrical equal-area projection with its standard parallel at the equator, making a flat-sky approximation valid to well within errors across the narrow survey region. The square map pixels are $0.5^{\prime}$ on a side at the equator. The maps used in this study are available on the Legacy Archive for Microwave Background Data Analysis222https://lambda.gsfc.nasa.gov/. In particular, the 277 GHz dataset is newly released as of this publication.

For details about ACT observations and mapmaking procedures, see Dünner et al. (2013). In summary, each detector timestream is analyzed and either retained or rejected based on a number of criteria (i.e., weather, detector performance, etc.). A preconditioned conjugate gradient solver produces a maximum likelihood map from these timestream data. We fit for an initial estimate of the point source signals. Models constructed from these initial estimates are then subtracted from the timestream data, which are then processed into a new, noise-dominated maximum likelihood map. The source models are then added back into the final map. This two-step treatment of the map helps prevent source power from biasing noise estimates used to produce the final maps, which in turn prevents biasing the point source signal in the map solution.

The overall flux density calibration of the map for each band is determined from cross-correlation of the CMB power spectrum at $300<\ell<1100$ with that measured from the Wilkinson Microwave Anisotropy Probe (WMAP) using the deepest ACT maps (the 2010 season). The uncertainty on the absolute temperature calibration of the 148 GHz band to WMAP is 2$\%$ (at $\ell=700$; Sievers et al. 2013; Hajian et al. 2011). Louis et al. (2014) cross-correlate the ACT maps with Planck maps and find excellent agreement.333The observed calibration factor between WMAP and Planck is 0.985, with Planck lower than WMAP. The calibration is then transferred to the 218 GHz data through a cross-correlation with the 148 GHz map. Relative calibration between seasons is performed using cross-correlations between the data of each ACT season. Details of this process can be found in Das et al. (2014). In addition to the overall calibration uncertainties, errors in the assumed instrument beam and the map-making can propagate to uncertainty in the recovered ACT flux densities. As discussed in Gralla et al. (2014), the overall systematic uncertainties on the flux density measurements for the 148 GHz and 218 GHz bands are 3% and 5%, respectively. These uncertainties dominate statistical uncertainties for the brightest sources in our sample. The calibration of the 277 GHz band derives from observations of Uranus (Hasselfield et al. 2013b). Because this method is less accurate than the CMB-based calibration of the lower frequency bands, the systematic error on the 277 GHz fluxes is 15%.

Part of the systematic flux density uncertainty is due to the fact that the telescope optical response depends on the source spectrum and whether the source is resolved (like the CMB) or point-like (e.g., Page et al. 2003; Swetz et al. 2011, Table 4). Publicly available ACT beams assume a CMB source spectrum, so their use in recovering point-source flux density is nuanced. In particular, the effective center of the bandpass for a given source depends on the convolution of its intrinsic spectrum with the instrument response over the band. As in Marsden et al. (2014), we have scaled the beam used in the matched filter and solid angle used in flux recovery to partially account for a shifted effective central frequency (148.65 GHz, 218.6 GHz, and 277.4 GHz for the three ACT bands). In Datta et al. (2019), a more detailed calculation was done to determine the range of flux density correction factors for different intrinsic source spectra. We have performed a similar analysis for the bands in this paper and estimated an associated systematic error in flux density recovery of $1.5$%.

The ACT sensitivity varies throughout the maps according to the depth of coverage. Each map was filtered with the beam appropriate for that season and band, as described below. The resulting calibrated, filtered maps were combined into a multi-season map via a weighted average, with the weights set for a given pixel by the integrated time that pixel was observed. Typical rms noise levels are 1.8, 2.4, and 5.2 mJy for 148, 218 and 277 GHz, respectively. Figure 1 shows the noise level across the main survey region for the 148 GHz band.

3 Methods

3.1 Spatial matched filtering

To optimize the signal-to-noise ratio (S/N) of the point-like sources, the ACT data were matched-filtered (e.g., Tegmark & de Oliveira-Costa 1998) with the ACT beam (Hasselfield et al. 2013b). The methods used are described fully in Marriage et al. (2011) and Marsden et al. (2014), which present catalogs of sources from the ACT southern surveys. Here we summarize the main steps of this analysis with an emphasis on unique features of these new catalogs.

The ACT brightness-temperature map is first multiplied by a weighting function $W(\bm{\theta})$ proportional to the square root of the number of observations per pixel to make the white noise per pixel approximately constant across the survey region.444Over the course of the season, observations with high and low noise distribute in similar ratios across the map, making the number of observations per pixel a good proxy for the inverse of the resulting noise variance. This has been confirmed empirically. Because there are fewer observations towards the edge of the map, this produces an inverse-noise-weighted map of brightness temperature $T(\bm{\theta})$ with a tapered window function and Fourier transform $\tilde{T}({\bf k})$.555${\bf k}=(k_{x},k_{y})$ is the angular wave vector, with $x$ and $y$ referring to right ascension and declination, respectively. This map is then filtered in the Fourier domain to produce a (weighted) matched-filtered map $T_{\mathrm{MF}}(\bm{\theta})$:

[TABLE]

where

[TABLE]

is the matched filter. Angular features scale as $d\theta\approx\pi/{k}$ (in radians); e.g., ${k}=3000$ corresponds to $3^{\prime}-4^{\prime}$ where sources begin to dominate over the CMB power (e.g., see Sievers et al. 2013). The function $\tilde{B}({\bf k)}$ (with units of steradian) is the Fourier transform of the “effective” instrument beam ($B(\bm{\theta})$, normalized such that $B(0)=1$), which takes into account the dependence of the beam on the source spectrum and telescope pointing jitter (Section 3.1.1). The beam is well approximated as azimuthally symmetric ($\tilde{B}({\bf k)}\approx\tilde{B}(k)$). The Fourier transform of the map data excluding the point sources, $\tilde{T}_{\mathrm{other}}$, includes atmosphere, detector noise, the CMB, and any other sources of brightness temperature that represent noise for the source signal. Unlike $\tilde{B}({\bf k)}$, $\tilde{T}_{\mathrm{other}}$ is not azimuthally symmetric. As in Marsden et al. (2014), in practice $\mid\tilde{T}_{\mathrm{other}}\mid^{2}$ is simply the power spectrum of the $W(\bm{\theta})$-weighted temperature data, which approximates the noise sources given the low amount of power in the point sources. A high-pass filter, $F_{k_{0},k_{x}}({\bf k})$, eliminates undersampled modes below $k_{0}=1000$ and modes with $|k_{x}|<100$, which are occasionally contaminated by telescope scan-synchronous noise.

Bright point sources in the maps can cause ringing from the matched filtering, which can introduce spurious low-S/N sources. We minimize this effect by initially searching for the brightest S/N $>50$ sources, cataloging these, and removing them from the original maps by filling in a $25\mathrm{\SIUnitSymbolArcminute}$ radius around each source position with a uniform flux density equal to the typical map noise. For the 148 GHz-selected sample, there are 41 such sources. We then refilter the maps without these bright sources, identifying all sources with S/N $>5$. The source candidates thus identified (both from the initial search and from the re-filtered maps) are passed to the next phase of the analysis in which source location and flux density are reconstructed.

Before estimating the flux density from a filtered map, the map is divided by $W(\bm{\theta})$ to undo the weighting applied for source detection and multiplied by the beam solid angle $\Omega_{B}$ to convert the brightness temperature to units of Jy/beam:

[TABLE]

where $I_{BB}$ is the Planck intensity function. The partial derivative of $I_{BB}$ evaluated at the CMB temperature converts the map $T_{\mathrm{MF}}$ from brightness temperature to intensity units, which are converted to flux density by the factor of the solid angle. For each detection, we extract a $0.04\mathrm{\SIUnitSymbolDegree}$-wide sub-map centered on the candidate source. In this submap, we remap the flux density from 0.5$\mathrm{\SIUnitSymbolArcminute}$ pixels using Fourier interpolation (zero padding in k space) into 16$\times$ smaller pixels ($\sim 2^{\prime\prime}$ on a side). The effect of averaging the source signal into pixels (“pixel windowing”) is also corrected. The new position and flux density estimation is associated with the maximum in the filtered map, now with finer pixelization. Finding a more accurate position for each source and correcting for pixel windowing is important for flux density recovery, especially for the higher-resolution 218 GHz and 277 GHz bands and for sources that do not lie near the centers of the larger pixels in the original, filtered map. Examples of the matched-filtered data are shown in Figure 3.

3.1.1 Effective multi-season beams

The instrument beam transform $\tilde{B}({\bf k)}$ is measured separately for each observing band and season using observations of Saturn and Uranus (Hasselfield et al. 2013b). The spectral shape of these planets does not match that of most of the compact sources in this analysis, so the effective central frequency of the bands is shifted for each spectral shape (planets, CMB, AGN, DSFGs). As reported by Swetz et al. (2011), the central frequencies are 147.6, 217.6, and 274.8 GHz for AGN and 149.7, 219.6, and 277.4 GHz for DSFGs. To take into account that our source samples include both AGN and DSFGs, we adopt fiducial central frequency values of 148.65, 218.6, and 277.4 GHz. These correspond to frequencies halfway between the central frequencies for steep-spectrum AGN and DSFGs for the 148 and 218 GHz bands. For the 277 GHz band, we simply adopted the DSFG central frequency. The same 148 and 218 GHz central frequencies were used in our previous ACT compact source analysis (Marsden et al. 2014). We rescale the beam widths to account for the shifts in these new effective central band frequencies. For more information on the effects of the beam on the calibration uncertainty, see Section 2.

The effective instrument beam is also broadened due to variations in pointing. We include this broadening by multiplying the instantaneous beam transform by $\exp(-k^{2}\sigma_{\theta}^{2}/2)$, where $\sigma_{\theta}=5\arcsec$ (Hasselfield et al. 2013b).

For 148 and 218 GHz, data from 2009 and 2010 were combined into single multi-season maps to increase the sensitivity to sources. These maps were filtered with the 2009 beams, but the choice of beam does not have a large effect. The FWHM of the beam changed by $<5\%$ from 2009 to 2010 for all bands. To investigate how this change affects flux density recovery, we simulated sources to have the shape of the 2010 beam and added these sources to the 2010 map, but then we filtered this map with the 2009 beam. The flux densities recovered from this simulated map are lower than those input by 1%. Because in the analysis the actual source shapes are some combination of the 2009 and 2010 beams, the flux densities of the sources in the multi-season catalogs are thus only affected at the sub-percent level. The brightest sources in the catalogs are typically blazars, and their emission varies from one season to the next by more than 1% (Section 5.3). The faintest sources’ statistical uncertainties are larger than 1%. Thus, the effect from filtering the combined maps with the 2009 beams is not significant. Individual season flux densities, for which the 2010 map is filtered with the 2010 beam, are also reported in the catalogs available online.

3.2 Multi-frequency matched filtering

To improve sensitivity to sources beyond the spatial matched filtering described above, we use the multifrequency information available from ACT. The methods we use extend beyond what has previously been done with ACT data. In this work, we specifically construct a multifrequency matched filter (MMF) (e.g., Melin et al. 2006) to search for faint, dusty galaxies. This choice is motivated by the availability of the 277 GHz data. Blazar detections are statistically dominated by the 148 GHz band, obviating the need for an MMF. However, both the 218 GHz and 277 GHz bands contribute significantly to the filtered DSFG signal, motivating the MMF for DSFG selection.

The MMF optimizes the S/N of a point source using data across multiple bands. As formulated in this work, the MMF produces a single map $T_{\mathrm{MMF}}(\bm{\theta})$ at a reference frequency $\nu_{0}$ from multiple maps $\{T_{\nu}(\bm{\theta})\}$ at multiple frequencies $\{\nu\}$. By analogy with the single-frequency matched filter (Equation 1), the MMF map is generated by applying a multi-component filter to the multi-frequency map set $\{T_{\nu}(\bm{\theta})\}$:

[TABLE]

where, in the fully general case, the filter functions $\Psi_{\nu}({\bf k})$ account for correlated noise between bands (e.g., correlations due to common modes from the CMB and atmospheric emission in ACT data). In principle, these correlations enter the formulation of the filter. In practice, for source selection at arcminute resolution, the strongly correlated modes at large angular scales can be neglected relative to the more abundant small-angular scale modes that are dominated by uncorrelated detector noise. The inter-band correlations from the CMB and the atmosphere dominate the uncorrelated detector noise for wave vectors ${k}<3000$, corresponding to angular scales $>3^{\prime}-4^{\prime}$. Given the arcminute resolution of the ACT 218 GHz and 277 GHz bands, approximately ten ACT beams fit within a correlated patch. In other words, there are ten times more beam-sized modes than modes for which CMB or atmosphere dominate. The effects of correlations are further minimized by the extra high-pass filter $F_{k_{0},k_{x}}$ in Equation 2, which de-weights the relatively few modes with $k<1000$. There remains residual correlated noise at high-$k$ (e.g., the unresolved cosmic infrared background) in the filtered ACT maps that presents a correlated noise source, but it is subdominant to detector noise. This is reflected by correlation coefficients: 0.13 between 148 and 218 GHz, 0.17 between 218 and 277 GHz, and 0.09 between 148 and 277 GHz. Thus significant gains in S/N are achieved with a simplified filter that assumes independent inter-band noise.

With independent inter-band noise, the MMF map (Equation 4) takes a simple, intuitive form. Working in flux-density-per-beam units (Equation 3), the MMF map is the weighted combination of the single-frequency matched-filtered maps $\{S_{\mathrm{MF};\nu}\}$ corresponding to the linear-least-squares best estimate of the flux density at the reference frequency:

[TABLE]

where the weights $w_{\nu}$ are

[TABLE]

In this equation, $\sigma_{\nu}^{2}(\bm{\theta})$ is the (position dependent) flux-density variance in the single-frequency matched-filtered map for frequency $\nu$. The constant $f_{\nu}$ encodes the assumed spectral energy distribution (SED) of the source, relating the flux density $S_{\nu}$ at frequency $\nu$ to the flux density $S_{\nu_{0}}$ at the reference frequency $\nu_{0}$:

[TABLE]

Finally, $\sigma_{MMF}^{2}(\bm{\theta})$ is the variance in the MMF map:

[TABLE]

Therefore, given the dominance of independent noise between ACT frequency bands, we can use this simplified formalism, availing ourselves of the tools developed for the single-frequency matched-filter described in Section 3.1.

For DSFGs, we take the 218 GHz map as the reference dataset and optimize the multi-frequency combination for a typical dusty source spectrum with spectral index $\alpha=3.77$. Thus the frequency scale factors (Equation 7) are $f_{148}=0.232$, $f_{218}=1.00$ and $f_{277}=2.47$. The multifrequency map generated was restricted to the region between $20^{\mathrm{h}}09^{\mathrm{m}}$ and $03^{\mathrm{h}}51^{\mathrm{m}}$ in R.A. and between $-1\arcdeg 33\mathrm{\SIUnitSymbolArcminute}$ and $1\arcdeg 24\mathrm{\SIUnitSymbolArcminute}$ in declination, where there was adequate coverage in the 277 GHz data. Filtered data in a subregion of the MMF area are shown in Figure 3 for the three single-frequency maps and the MMF map. Equation 8 predicts a noise level in the MMF, referenced to 218 GHz, of 1.6 mJy, however the measured level is 1.9 mJy (compared to the 2.4 mJy noise in the single-frequency map). The full sensitivity improvement is not achieved due to residual noise correlations between bands, which make Equation 6 suboptimal. In the end, the cleanest comparison between the MMF and 218 GHz single frequency approach is in terms of S/N: for DSFGs in the MMF-derived catalog (Section 4), the median improvement in S/N of the MMF over the 218 GHz data alone is 1.30.

3.3 Detection, selection, localization, and flux-density recovery

In Section 4, we introduce a catalog in which sources are detected in multiple maps. As discussed in Section 3.1, a source is detected in a map if its S/N $>5$. By this definition, a source may be detected in a combination of single-frequency filtered maps and the MMF map tuned for a dust-like spectrum. We further identify each source with the dataset in which it is detected with the highest S/N. This “selection map” defines the selection function and also provides the most precise location of the source. The dataset in which a source is selected also plays into flux density debiasing (Section 4.3).

To estimate flux densities and associated errors, the single-frequency filtered maps $T_{\mathrm{MF},\nu}$ are used. In a $4^{\prime}$ patch centered on each source, the map $T_{\mathrm{MF},\nu}$ is reprojected with Fourier interpolation to ($\sim 2^{\prime\prime}$) pixels, correcting for the signal-dilution effect of the pixel window function. Then the flux density is obtained from this finer-pixelized map at the selection-map-determined source location (i.e., by “forced photometry”). Thus even if a source is undetected in a single-frequency map, it will still have an associated (low S/N) flux estimate.

Note that if the selection map is itself a single-frequency map, then that map will be used to determine the source’s selection function, location, and flux density. The MMF map is never used for flux density estimation, only source selection and localization. Conversely, due to its high noise, the 277 GHz map is never used for selection or localization.