Edinburgh Research Explorer The SCUBA-2 Cosmology Legacy Survey: the nature of bright submm galaxies from 2 deg2 of 850-m imaging Citation for published version: Michaowski, MJ, Dunlop, JS, Koprowski, MP, Cirasuolo, M, Geach, JE, Bowler, RAA, Mortlock, A, Caputi, KI, Aretxaga, I, Arumugam, V, Chen, C-C, McLure, RJ, Birkinshaw, M, Bourne, N, Farrah, D, Ibar, E, van der Werf, P & Zemcov, M 2017, 'The SCUBA-2 Cosmology Legacy Survey: the nature of bright submm galaxies from 2 deg2 of 850-m imaging', Monthly Notices of the Royal Astronomical Society, vol. 469, no. 1, pp. 492-515. https://doi.org/10.1093/mnras/stx861 Digital Object Identifier (DOI): 10.1093/mnras/stx861 Link: Link to publication record in Edinburgh Research Explorer Document Version: Peer reviewed version Published In: Monthly Notices of the Royal Astronomical Society General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 27. Mar. 2021
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Edinburgh Research Explorer
The SCUBA-2 Cosmology Legacy Survey: the nature of brightsubmm galaxies from 2 deg2 of 850-m imagingCitation for published version:Michaowski, MJ, Dunlop, JS, Koprowski, MP, Cirasuolo, M, Geach, JE, Bowler, RAA, Mortlock, A, Caputi,KI, Aretxaga, I, Arumugam, V, Chen, C-C, McLure, RJ, Birkinshaw, M, Bourne, N, Farrah, D, Ibar, E, vander Werf, P & Zemcov, M 2017, 'The SCUBA-2 Cosmology Legacy Survey: the nature of bright submmgalaxies from 2 deg2 of 850-m imaging', Monthly Notices of the Royal Astronomical Society, vol. 469, no. 1,pp. 492-515. https://doi.org/10.1093/mnras/stx861
Digital Object Identifier (DOI):10.1093/mnras/stx861
Link:Link to publication record in Edinburgh Research Explorer
Document Version:Peer reviewed version
Published In:Monthly Notices of the Royal Astronomical Society
General rightsCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s)and / or other copyright owners and it is a condition of accessing these publications that users recognise andabide by the legal requirements associated with these rights.
Take down policyThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorercontent complies with UK legislation. If you believe that the public display of this file breaches copyright pleasecontact [email protected] providing details, and we will remove access to the work immediately andinvestigate your claim.
Mon. Not. R. Astron. Soc. 000, 1–25 (2016) Printed 31 July 2018 (MN LATEX style file v2.2)
The SCUBA-2 Cosmology Legacy Survey: the nature of
bright submm galaxies from 2 deg2 of 850-µm imaging
Micha l J. Micha lowski1⋆, J. S. Dunlop1, M. P. Koprowski1,2, M. Cirasuolo3,4,1,
J. E. Geach2, R. A. A. Bowler1,5, A. Mortlock1, K. I. Caputi6, I. Aretxaga7,
V. Arumugam3,1, Chian-Chou Chen8,3, R. J. McLure1, M. Birkinshaw9,10,
N. Bourne1, D. Farrah11, E. Ibar12, P. van der Werf13, M. Zemcov141SUPA†, Institute for Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh, EH9 3HJ, UK2Center for Astrophysics Research, Science and Technology Research Institute, University of Hertfordshire, Hatfield AL10 9AB, UK3European Southern Observatory, Karl Schwartzchild Strasse 2, D-85748 Garching, Germany4UK Astronomy Technology Centre, Royal Observatory, Edinburgh EH9 3HJ, UK5Astrophysics, The Denys Wilkinson Building, University of Oxford, Keble Road, Oxford OX1 3RH, UK6Kapteyn Astronomical Institute, University of Groningen, PO Box 800, NL-9700 AV Groningen, The Netherlands7Instituto Nacional de Astrofısica, Optica y Electronica (INAOE), Aptdo. Postal 51 y 216, 72000 Puebla, Pue., Mexico8Centre for Extragalactic Astronomy, Department of Physics, Durham University, South Road, Durham DH1 3LE, UK9HH Wills Physics Laboratory, University of Bristol, Tyndall Avenue, Bristol BS8 1TL, UK10Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138, USA11Department of Physics, Virginia Tech, Blacksburg, VA 24061, USA12Instituto de Fısica y Astronomıa, Universidad de Valparaıso, Avda. Gran Bretana 1111, Valparaiso, Chile13Leiden Observatory, Leiden University, PO Box 9513, NL-2300 RA Leiden, the Netherlands14Center for Detectors, School of Physics and Astronomy, Rochester Institute of Technology, Rochester, NY 14623, USA
Accepted 2017 April 5. Received 2017 March 30; in original form 2016 October 7.
ABSTRACT
We present physical properties [redshifts (z), star formation rates (SFRs) and stellar
masses (Mstar)] of bright (S850 > 4mJy) submm galaxies in the ≃ 2 deg2 COSMOSand UDS fields selected with SCUBA-2/JCMT. We complete the galaxy identificationprocess for all (≃ 2 000) S/N > 3.5 850-µm sources, but focus our scientific analysison a high-quality subsample of 651 S/N > 4 sources with complete multiwavelengthcoverage including 1.1-mm imaging. We check the reliability of our identifications,and the robustness of the SCUBA-2 fluxes by revisiting the recent ALMA follow-upof 29 sources in our sample. Considering > 4mJy ALMA sources, our identificationmethod has a completeness of ≃ 86 per cent with a reliability of ≃ 92 per cent, andonly ≃ 15–20 per cent of sources are significantly affected by multiplicity (when asecondary component contributes > 1/3 of the primary source flux). The impact ofsource blending on the 850-µm source counts as determined with SCUBA-2 is modest;scaling the single-dish fluxes by ≃ 0.9 reproduces the ALMA source counts. For ourfinal SCUBA-2 sample, we find median z = 2.40+0.10
−0.04, SFR = 287 ± 6M⊙ yr−1 andlog(Mstar/M⊙) = 11.12± 0.02 (the latter for 349/651 sources with optical identifica-tions). These properties clearly locate bright submm galaxies on the high-mass end ofthe ‘main sequence’ of star-forming galaxies out to z ≃ 6, suggesting that major merg-ers are not a dominant driver of the high-redshift submm-selected population. Theirnumber densities are also consistent with the evolving galaxy stellar mass function.Hence, the submm galaxy population is as expected, albeit reproducing the evolu-tion of the main sequence of star-forming galaxies remains a challenge for theoreticalmodels/simulations.
Since their discovery almost twenty years ago (Smail et al.1997; Hughes et al. 1998; Barger et al. 1998), the na-ture of galaxies selected at submillimetre (submm) wave-
lengths (submm galaxies), and their role in galaxy evo-lution, has been the subject of extensive study (seeCasey, Narayanan, & Cooray 2014 and Blain et al. 2002 forreviews). Of particular importance is the determination ofthe mechanism that drives the huge star formation rates(SFRs, and hence huge far-infrared luminosities) of thesegalaxies, in order to better understand their formation andsubsequent evolution.
This can be studied using various different diagnos-tics, including the location of galaxies on the stellar mass(Mstar) versus SFR plane. At a given redshift, normalstar-forming galaxies form a so-called main sequence onthis plane (with near constant specific star formation rate,sSFR ≡ SFR/Mstar), whereas ‘starbursts’ are offset to-wards higher sSFRs by a factor of > 2–4 (Daddi et al.2007; Noeske et al. 2007; Gonzalez et al. 2010; Elbaz et al.2011; Speagle et al. 2014). Hence, the location of submmgalaxies with respect to the main sequence may tell uswhether they are predominantly triggered by mergers, oralternatively are fed by (relatively steady) cold gas in-fall (the two options proposed by theoretical arguments;Swinbank et al. 2008; Dave et al. 2010; Narayanan et al.2010, 2015; Ricciardelli et al. 2010; Gonzalez et al. 2011;Hayward et al. 2011a,b, 2012; Cowley et al. 2015). This isbecause a major merger is a short-lived phenomenon, re-sulting in a substantial but temporary boost in SFR, po-tentially pushing a galaxy significantly above the main se-quence (e.g. Hung et al. 2013; cf. Forster Schreiber et al.2009). Recent simulations show that high-redshift gas-richmergers result in the SFR enhancement by a factor of ∼ 2–5(Fensch et al. 2017, their figs 5–7), so if submm galaxies arepredominantly powered by major mergers, then they shouldby offset from the main sequence by this factor.
There is still some debate over whether submm galax-ies lie above the main sequence, or simply form itshigh-mass end. This debate is not primarily concernedwith the form of the main sequence, as most studiesagree that, at high redshifts, the main sequence con-tinues to extend to high stellar masses with SFR ∝Mx
∗ , where x is in the range 0.75–1.0 (Karim et al. 2011;Speagle et al. 2014; Renzini & Peng 2015; Schreiber et al.2015; Koprowski et al. 2016; Dunlop et al. 2017) with noevidence of any break as has been suggested at lower red-shifts (Oliver et al. 2010; Whitaker et al. 2014; Ilbert et al.2015; Lee et al. 2015; Tomczak et al. 2016), or from galaxysurveys based purely on optical data (Kochiashvili et al.2015; Tasca et al. 2015). Based on morphological decom-position at low redshifts this break was shown to dis-appear when only disc (not bulge) stellar mass wasused (Abramson et al. 2014). Low stellar mass estimatesfor submm galaxies lead to high sSFRs, above themain sequence (Hainline et al. 2011; Wardlow et al. 2011;Magnelli et al. 2012; Casey et al. 2013), whereas higher de-rived stellar masses place submm galaxies on the main se-quence (Micha lowski et al. 2010a, 2012b, 2014a; Yun et al.2012; Johnson et al. 2013; Koprowski et al. 2014, 2016). InMicha lowski et al. (2012a) we showed that this discrepancyresults largely from different assumptions concerning theparametrization of star formation histories in the spectralenergy distribution (SED) modelling. In particular, two-component star formation histories result in higher stellarmasses. Such a choice assumes that galaxies before the be-
ginning of the submm galaxy phase (either a peak of the gasaccretion or a merger) have already built up a substantialfraction of their current stellar mass. In Micha lowski et al.(2014a) we showed that two-component star formation his-tories (resulting in higher stellar masses) provide the mostaccurate stellar masses for a sample of simulated submmgalaxies, which have properties that agree well with manyproperties of real submm galaxies (Micha lowski et al. 2014a,and references therein).
Hence, our studies of medium-size samples of arounda hundred submm galaxies resulted in the conclusion thatthey form the high-mass end of the main sequence, atleast at z . 3–4 (Micha lowski et al. 2012b; Koprowski et al.2014, 2016). A similar conclusion has been drawn from re-cent hydrodynamical simulations showing that all observa-tional properties of submm galaxies can be explained bynon-merging massive galaxies that sustain high SFRs foraround 1 Gyr, and do not leave the main sequence dur-ing that time (Narayanan et al. 2015; see also Dave et al.2010; Hayward et al. 2011a; Shimizu et al. 2012). However,other simulations predict that a significant fraction ofsubmm galaxies are powered by violent starbursts result-ing from mergers (Baugh et al. 2005; Narayanan et al. 2010;Hayward et al. 2013). Further observational studies basedon larger samples of submm galaxies are required to clarifythis issue.
In addition, rather little is known about the very high-redshift (z > 4) tail of the submm galaxy population, be-cause to date only a handful of submm sources have beenconfirmed at these extreme redshifts (Coppin et al. 2009;Capak et al. 2008, 2011; Schinnerer et al. 2008; Daddi et al.2009b,a; Knudsen et al. 2008, 2010; Riechers et al. 2010,2013; Cox et al. 2011; Smolcic et al. 2011; Combes et al.2012; Walter et al. 2012; Dowell et al. 2014; Watson et al.2015). The discovery and study of such sources is difficultfor several reasons. First, these very high-redshift sourcesare intrinsically rare, so very few of them are likely tobe discovered in submm surveys covering only a few hun-dred square arcmin (as typically achieved at 850µm priorto SCUBA-2). Secondly, the combined effects of extremedust-obscuration and redshift mean that optical and radiocounterparts can be extremely faint (e.g. Walter et al. 2012),and hence redshift information hard to secure. Moreover,the determination of redshifts at submm/mm wavelengthsfrom carbon monoxide (CO) lines currently remains verytime consuming for all but the brightest objects (Weiß et al.2009a, 2013; Vieira et al. 2013), and hence is not practi-cal for large samples. These difficulties, and the resultingsmall samples of confirmed high-redshift submm galaxieshave also hampered the proper statistical investigation ofsuggestions that the brightest submm sources are prefer-entially found at the highest redshifts (Ivison et al. 2002;Pope et al. 2005; Micha lowski et al. 2012b; Koprowski et al.2014; Simpson et al. 2014).
Our understanding of both the relation of submm galax-ies with respect to the main sequence, and the prevalenceand nature of the most extreme redshift submm sources canboth be improved by the larger area submm surveys nowbeing provided by SCUBA-2. Hence, here we use the largestdeep survey at 850 µm undertaken to date, the SCUBA-2Cosmology Legacy Survey (CLS). This survey is described,and the 850µm catalogues are presented in Geach et al.
(2017). The results from smaller, deeper sub-fields withinthe CLS have already been presented in Geach et al.(2013), Roseboom et al. (2013), Koprowski et al. (2016) andZavala et al. (2017), while multiwavelength identifications(IDs) for the sources in the ∼ 1 deg2 UDS field have beenprovided by Chen et al. (2016).
Here we build on this work by attempting to deter-mine the identifications, redshifts and physical propertiesof a statistically significant, well-defined sample of around2000 submm galaxies detected in the full ∼ 2 deg2 of 850-µmimaging provided by the S2CLS across the UDS and COS-MOS fields. A key objective of this study is to assemblea substantial but well-defined subsample of submm sourceswith complete redshift information, in order to better definethe high-redshift tail of the population, and to clarify theextent to which submm galaxies can indeed be naturally ex-plained by the high-mass end of the evolving main sequenceof star-forming galaxies.
This paper is structured as follows. In Section 2 wesummarise the submm imaging, and describe the support-ing higher-resolution multiwavelength data (optical/near-IR/mid-IR/radio) that we utilise to establish the positionsof the galaxy counterparts to the submm sources in thetwo survey fields. In Section 3, we describe the methodsused to identify potential galaxy counterparts, and to assesstheir statistical significance/robustness. In Section 4 we thenpause to revisit the results of existing ALMA follow-up of 29of the sources in our sample, both to assess the robustnessand completeness of our identification process, and to assessthe impact of source multiplicity/blending on the reliabilityof the 850-µm source counts. In Section 5 we discuss andpresent the long-wavelength imaging available in our sur-vey fields; such information is crucial for the estimation ofredshifts for sources that lack optical/near-IR counterparts,and for the estimation of SFRs, and leads us to define asubset of 651 sources with the information required for anunbiased investigation of their physical properties (i.e. with> 4σ detections at 850 µm, and sufficient multiwavelengthdata to yield complete/unbiased redshift information). Thephotometric redshifts, and source number density as a func-tion of redshift are derived in Section 6, while SFRs andstellar masses are presented in Section 7. We discuss theimplications of our results in Section 8, and close with ourconclusions in Section 9. We use a cosmological model withH0 = 70 km s−1 Mpc−1, ΩΛ = 0.7 and Ωm = 0.3, and giveall magnitudes in the AB system (Oke 1974).
2 DATA
2.1 Submm
We used the 850µm data obtained with the JamesClerk Maxwell Telescope (JCMT) equipped with the Sub-millimetre Common-User Bolometer Array 2 (SCUBA-2;Holland et al. 2013) within the Cosmology Legacy Survey(CLS; Geach et al. 2017). The SCUBA-2 data were reducedwith the Smurf
1 package V1.4.0 (Chapin et al. 2013) withthe flux calibration factor (FCF) of 537 Jy pW−1 beam−1
1 www.starlink.ac.uk/docs/sun258.htx/sun258.html
Table 1. The 3σ depths of the multifrequency data used in theCOSMOS and UDS fields.
Filter COSMOS UDS Unit
u 27.1 · · · AB magB · · · 27.8 AB magV · · · 27.4 AB magg 27.2 · · · AB magr 26.7 · · · AB magR · · · 27.1 AB magi 26.4 27.0 AB magz′ 25.3 26.3 AB magY 25.0/25.6a 25.1 AB mag
a The two alternative values correspond to the shallower anddeeper strips of the UltraVISTA near-IR imaging.
(Dempsey et al. 2013). The full width at half-maximum(FWHM) of the resulting 850µm map is 14.6 arcsec.
For this study we have used the ‘wide’ SCUBA-2 850µmmaps of the COSMOS (1.22 deg2 reaching ≃ 1.4 mJy rms)and UDS (0.96 deg2 reaching ≃ 0.9 mJy rms) fields. Theywere selected because they are the two largest CLS fieldscorresponding to ∼ 70% of the total survey area, and be-cause in most of the other (smaller) fields the auxiliary dataare shallower, making it more difficult to constrain physi-cal properties of submm galaxies. The source catalogue ispresented in Geach et al. (2017), who extracted the sourcesby searching for peaks in the beam-convolved map with asignal-to-noise ratio > 3.5σ. This process resulted in 726 and1088 sources in the COSMOS and UDS fields, respectively.The source S2CLSJ021830-053130 with an 850µm flux of∼ 50 mJy is the lensed candidate discussed by Ikarashi et al.(2011).
2.2 Radio and mid-infrared
The Karl G. Jansky Very Large Array (VLA) 1.4 GHz ra-dio data were taken from Schinnerer et al. (2007, 2010) forthe COSMOS field, and from Ivison et al. (2005, 2007) andArumugam et al. (in preparation) for the UDS field. Thecatalogues include sources for which > 3σ detections wereobtained.
The mid-infrared (mid-IR) Spitzer (Werner et al. 2004;Fazio et al. 2004; Rieke et al. 2004) data are from theSpitzer Extended Deep Survey (SEDS; Ashby et al. 2013),the Spitzer Large Area Survey with Hyper-Suprime-
Cam (SPLASH, PI: P. Capak), the S-COSMOS project(Sanders et al. 2007; Le Floc’h et al. 2009) and the SpitzerPublic Legacy Survey of the UKIDSS Ultra Deep Survey(SpUDS; PI: J. Dunlop)2 described in Caputi et al. (2011).To obtain the 3.6 and 4.5µm photometry we used the de-confusion code T-PHOT3 (Merlin et al. 2015). This utilisesprior information on the positions and morphologies of ob-jects from a high-resolution image (HRI; in this case theK-band or Ks-band images) to construct a model of a givenlow-resolution image (LRI; in this case the Spitzer imaging)while solving for the fluxes of these objects.
The 3σ depths of the VLA radio and Spitzer mid-IRimaging in both fields are summarised in Table 1.
2.3 Optical and near-infrared
The optical data in both fields were obtained withSubaru/SuprimeCam (Miyazaki et al. 2002), as describedin Dye et al. (2006) and Furusawa et al. (2008), andfrom the Canada-France-Hawaii Telescope Legacy Survey(CFHTLS), as described in Bowler et al. (2012). The deepz′-band images are described in Bowler et al. (2012) andFurusawa et al. (2016). The near-infrared (near-IR) data inthe COSMOS field was obtained from Data Release 2 of theUltraVISTA survey (McCracken et al. 2012; Bowler et al.2014), while in the UDS field the near-IR data were providedby Data Release 10 of the UKIRT Infrared Deep Sky Sur-vey (UKIDSS; Lawrence et al. 2007; Cirasuolo et al. 2010;Fontana et al. 2014).
In both fields the optical and near-IR fluxes were mea-sured in 3-arcsec diameter apertures, and the resulting 3σdepths of this aperture photometry are summarised in Ta-ble 1.
Finally, we used a list of spectroscopic redshiftsfrom 3D-HST (Brammer et al. 2012; Skelton et al. 2014;Momcheva et al. 2016), VIMOS Ultra-Deep Survey (VUDS;Le Fevre et al. 2015; Tasca et al. 2017), zCOSMOS(Lilly et al. 2007, 2009), MOSFIRE Deep Evolution Field(MOSDEF; Kriek et al. 2015), PRIsm MUlti-object Survey(PRIMUS; Coil et al. 2011) and from Trump et al. (2009,2011) in the COSMOS field and UDSz (McLure et al. 2013;Bradshaw et al. 2013, Almaini et al., in preparation) andVIMOS Public Extragalactic Redshift Survey (VIPERS;Guzzo et al. 2014) in the UDS field.
3 GALAXY IDENTIFICATIONS
As in Micha lowski et al. (2012b), we obtained the radio,24µm and 8µm counterparts applying the method out-lined in Downes et al. (1986), Dunlop et al. (1989) andIvison et al. (2007). We applied a uniform search radiusof 8 arcsec, a conservatively high value in order to allowfor astrometry shifts due to either pointing inaccuraciesor source blending. This is an appropriate choice for theJCMT/SCUBA-2 850µm beam FWHM of ≃ 15 arcsec, asALMA observations have revealed the brightest submm
sources up to approximately half the beam FWHM awayfrom the original JCMT/SCUBA-2 and APEX/LABOCApositions (Simpson et al. 2015b; Hodge et al. 2013).
The statistical significance of each potential counterpartwas assessed on the basis of the corrected Poisson probabilityp that the chosen radio, 24µm or 8µm candidate could havebeen selected by chance. IDs with a probability of chanceassociation of p 6 0.05 are deemed to be ‘robust’, whereasthose with 0.05 < p 6 0.1 are labelled as ‘tentative’. If thep values of multiple IDs for a given SCUBA-2 source satisfythese criteria, then all are retained, but the one with thelowest p value is used for subsequent analysis.
IDs for the SCUBA-2 sources in the UDS field basedon radio and optical counterparts (utilising an optical/near-IR colour selection) have previously been presented byChen et al. (2016). In this work, following our previous prac-tice, we have complemented radio counterpart selection withsearches for counterparts in the 24-µm and 8-µm Spitzerimaging. Nevertheless, the agreement between our IDs inthe UDS field and those presented by Chen et al. (2016) isvery good; restricting the SCUBA-2 sample to the 716 > 4σobjects in the UDS field, only 90 of our robust (p 6 0.05)primary IDs (with the lowest p) are not matched to those ofChen et al. (2016), and 29 of these 90 are assigned Class = 2by Chen et al. (2016), meaning that the optical data wereinadequate for searching for IDs for these sources in theChen et al. (2016) study.
All of our IDs for the > 3.5σ 850µm sources in theCOSMOS and UDS fields are presented in Tables A1 andA2 in the appendix, respectively.
We summarise the outcome of the identification pro-cess in Table 2, where we give the number of SCUBA-2sources with IDs, and the nature of these IDs. We presentthe ID statistics split by ID wavelength and robustness,and also tabulate the results for three different significancecuts in the 850-µm source sample. The number of IDs as afunction of the SCUBA-2 850µm flux is plotted in Fig. 1(shown here for the full > 3.5σ SCUBA-2 sample). The IDrate is lower towards lower submm fluxes. This is expected,both because of the increasing prevalence of false and/orflux-boosted sources at low significance, but also becausefainter submm galaxies have, on average, correspondinglylower radio and mid-IR fluxes (as expected if the SED shapedoes not vary strongly from source to source; see fig. 1 ofMicha lowski et al. 2012b and of Ibar et al. 2010).
Unsurprisingly, the fraction of SCUBA-2 sources thatlack IDs is also a function of 850-µm S/N. As mentionedabove, this is partly because the lower S/N sources are gener-ally fainter, but also, as Geach et al. (2017) have shown fromsource injection and retrieval simulations, approximately15–20 per cent of ≃ 3.5σ SCUBA-2 sources located by thepeak-finding method in these wide-area survey fields are ei-ther completely erroneous or substantially flux-boosted. Itis thus perhaps as expected that the unidentified fraction(with neither robust nor tentative IDs) drops from ≃ 35 percent at S/N > 3.5 to ≃ 20 per cent at S/N > 5.0 (where thepercentage of false positive sources is expected to be < 1per cent; Geach et al. 2017). Despite this, we provide theIDs for all sources in the 3.5σ catalogue because, as Table 2quantifies, the extended sample provides a large number ofadditional robust identifications worthy of further study and
Table 2. Galaxy counterpart identification statistics for SCUBA-2 sources in the UDS and COSMOS fields, detailing success rates forboth robust and tentative IDs, split by wavelength, and also tabulated for three different significance cuts in the original 850-µm sample.
Field N rob. ID tent. ID No ID N1.4 rob1.4 tent1.4 N24 rob24 tent24 N8 rob8 tent8 Nopt zopt# (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%)
(1) field name; (2) the total number of SCUBA-2 sources, (3) the number of sources with IDs having at least one robust associationwith p 6 0.05 at radio, 24µm, or 8.0µm; (4) the number of sources with IDs having at least one tentative counterpart with
0.05 < p < 0.1; (5) the number of sources with no IDs; (6) the number of SCUBA-2 sources covered by the radio map (for which radioIDs can in principle be obtained); (7) and (8) the number of robust and tentative 1.4GHz IDs; (9) the number of SCUBA-2 sources
covered by the 24 µm map (for which 24µm IDs can in principle be obtained); (10) and (11) the number of robust and tentative 24µmIDs; (12) the number of SCUBA-2 sources covered by the 8.0µm map (for which 8.0µm IDs can in principle be obtained); (13) and(14) the number of robust and tentative 8.0µm IDs; (15) the number of SCUBA-2 sources covered by the optical map (for which
optical redshift can in principle be derived); (16) the number of SCUBA-2 sources with the best ID having an optical redshift. In theparentheses the percentage of IDs are shown.
1
10
100
N
All IDs
2 4 6 8 10 12F850 / mJy
1
10
100
N
p<0.05 IDs
COSMOS
No ID8.0 µm ID only24 µm ID (not radio)Radio ID
1
10
100
N
All IDs
2 4 6 8 10 12F850 / mJy
1
10
100
N
p<0.05 IDs
UDS
No ID8.0 µm ID only24 µm ID (not radio)Radio ID
Figure 1. The number of IDs as a function of SCUBA-2 850-µm flux density for the COSMOS and the UDS fields (left and right,respectively). The red histogram shows the number of SCUBA-2 sources with radio IDs. The space between the red histogram and theblue histogram shows the number of SCUBA-2 sources with 24-µm IDs but no radio IDs. The space between the blue histogram andthe green histogram shows the number of SCUBA-2 sources with only 8-µm IDs. The space between the green histogram and the blackhistogram shows the number of SCUBA-2 sources with no IDs. The upper panels take into account all IDs, whereas the lower panels takeinto account only robust (p 6 0.05) IDs. The sharp decline in the number of sources in the UDS field below 3mJy reflects the highlyuniform depth of the SCUBA-2 map in this field. This map is also deeper than that of the COSMOS field. The histograms shown herecontain all 1814 sources in the full > 3.5σ SCUBA-2 sample.
follow-up. We therefore provide positions of all new IDs inthe appendix.
Nevertheless, it would clearly be wrong to infer thatthe real fraction of unidentified sources is as large as ≃ 35per cent, when the evidence from the higher S/N cuts sug-gests the true figure is ≃ 20−25 per cent. Consequently, forthe remainder of the analysis in this paper we consider onlysources with S/N > 4.0 (where the false positive SCUBA-2 source rate is expected to be ≃ 5 per cent; Geach et al.2017). At this S/N threshold, Table 2 shows we have robustIDs for ≃ 60 per cent of the 1121 sources, with an addi-tional ≃ 15 per cent having tentative IDs, and hence ≃ 25per cent of sources remaining unidentified. About half of therobust IDs are provided by the 1.4 GHz radio imaging, andso extending the ID process to search for counterparts inthe 24-µm and 8-µm imaging has had a significant positiveimpact. We note that the ID statistics in the COSMOS andUDS fields are statistically consistent (due to the homogene-ity of the SCUBA-2 data set, and the similar quality of thesupporting data in the two survey fields).
In summary, we have completed the ID process and,for the ≃ 1000-source > 4σ 850-µm sample, have identified≃ 75 per cent of the sources. A key question, then, is why≃ 25 per cent of the SCUBA-2 sources remain unidentified.There are several possible factors. First, some small remain-ing subset of these sources may not be real. Secondly, asdiscussed further below, a few of these sources may in factbe blends of 2 or 3 significantly fainter sources, for whichthe optical/IR/radio counterparts lie below the flux-densitylimits of the supporting data; this is arguably not a seriousproblem since such sources should not really be retained in abright flux-limited sample. Finally, some of the unidentifiedsources are likely to lie at higher redshifts where the resultingradio and mid-IR flux densities are too faint for their coun-terparts to be uncovered in the existing VLA and Spitzerimaging (which, unlike the submm imaging, does not ben-efit from a negative k-correction). In the following sectionswe explore these issues further, first by revisiting the resultsof ALMA follow-up of a subset of the SCUBA-2 sources, andthen by exploiting the available long-wavelength (FIR–mm)data in the field to attempt to constrain the redshifts of theunidentified SCUBA-2 sources.
4 COMPARISON WITH ALMA FOLLOW-UP
4.1 Validation of galaxy identifications
We can estimate the completeness and reliability of ouridentification procedure by considering the subsample of 29SCUBA-2 sources in our sample that has already been thesubject of deep ALMA follow-up imaging (Simpson et al.2015b). Although this subsample was originally selected tocontain the brightest SCUBA-2 sources in the UDS field, thefinal deeper imaging from the S2CLS corrects for some of themore severe flux-boosting effects in the earlier map, with theconsequence that this subset of sources actually contains ob-jects with flux densities extending down to the flux-densitylimit of our sample (and is thus more representative of theoverall sample than originally anticipated).
In Fig. 2 we plot the ALMA flux densities of all52 ALMA galaxies versus the SCUBA-2 flux densities of
0 5 10 15FSCUBA2 / mJy
0
5
10
15
FA
LMA /
mJy
All ALMA objectsOur IDs
Figure 2. The ALMA flux densities for ALMA sources (redand blue squares) revealed through the follow-up of 29 SCUBA-2sources in the UDS field (Simpson et al. 2015b), plotted againstthe SCUBA-2 single-dish flux density of each source as derivedfrom the final SCUBA-2 CLS 850-µm imaging of the UDS field.The ALMA sources lying within the SCUBA-2 FWHM in thefollow-up imaging are connected by solid vertical lines, and theblue squares indicate which of the ALMA sources was identi-fied by our radio+mid-IR identification process as the locationof the galaxy making the dominant contribution to the SCUBA-2submm source. Although the brightest SCUBA-2 source dividesinto two ALMA subcomponents of comparable flux density, it canbe seen that, in the vast majority of cases, the secondary ALMAcomponent is a much fainter (≃ 1–2mJy) object in the field.Moreover, the flux densities of the secondary components are notcorrelated with the brighter component flux densities, whereasthe ALMA and SCUBA-2 flux densities of the brighter compo-nents are well correlated and frequently near equal (as indicatedby the diagonal dashed line). For 25 of the 29 SCUBA-2 sources,our radio+mid-IR identification process correctly locates the po-sition of the dominant ALMA component, yielding an estimatedcompleteness of ≃ 86 per cent.
the corresponding SCUBA-2 sources and highlight in bluewhere, utilising the radio/mid-IR ID method adopted here,we have successfully located the position of the galaxy coun-terpart as confirmed by ALMA. For many of the sources theALMA imaging has revealed more than one submm com-ponent, and in Fig. 2 we show this by connecting ALMAsubcomponents with solid vertical lines. In the majority ofcases it can be seen that the secondary ALMA componentis a much fainter (≃ 1–2 mJy) object in the field (i.e. ly-ing within the SCUBA-2 FWHM), and that the flux den-sities of the secondary components are not correlated withthe brighter component flux densities. For such faint submmgalaxies we do not expect to be able to identify many galaxycounterparts given the depth of the supporting imaging, butthat is not a concern for this study that is focused on thestudy of sources with S850 > 4 mJy. The key point is thatour identification method has correctly identified the po-sition of the brighter ALMA counterpart for 25/29 of thesources, yielding a completeness of ≃ 86 per cent.
We can also use this control sample to estimate the reli-ability of our ID method (i.e. the fraction of IDs confirmed byALMA). We have identified 25 robust IDs (21 primary) forthe 29 SCUBA-2 sources with ALMA follow-up. Of these,
Figure 3. The cumulative distribution of single-dishJCMT/SCUBA-2 850-µm flux densities (blue) and interfer-ometric ALMA flux densities (red) resulting from the follow-upimaging of 29 SCUBA-2 sources in the UDS field (Simpson et al.2015b), as already illustrated in Fig. 2. Although the ALMAimaging reveals a population of fainter sources lying below theflux-density limit of the SCUBA-2 imaging, the bright end ofthe source counts is relatively little affected by whether oneutilises the original SCUBA-2 flux densities, or those of thebrighter ALMA subcomponents. Even without any correction,the flux distributions brightwards of S850 ≃ 4mJy are notsignificantly different (application of the Kolmogorov-Smirnovtest yields a probability of only 50 per cent that the ALMAand SCUBA-2 flux densities are not drawn from the sameparent population), but application of a modest correction,either subtracting ≃ 1mJy from all SCUBA-2 flux densitiesor, as shown here, multiplying the SCUBA-2 flux densities by0.9 (green distribution) is sufficient to bring the SCUBA-2 andALMA bright source counts into near perfect agreement.
23 (20) are confirmed by ALMA, while two are not. Thisyields a reliability of ≃ 92 per cent that our primary galaxyidentifications correspond to submm sources. We have alsoidentified six tentative ID (three primary), out of which twowere confirmed by ALMA.
It might be argued that these estimates of complete-ness and reliability could be optimistic, because the ALMAcontrol sample utilised here remains biased towards highersubmm flux densities than the full UDS and COSMOS sam-ples. However, Fig. 2 shows that we correctly identified threeof the four faintest sources in the control sample, and we re-iterate that we are not concerned with identifying sources(either SCUBA-2 sources, or ALMA subcomponents) signifi-cantly fainter than S850 ≃ 4 mJy. Moreover, the high successrate of the radio+mid-IR identification approach has alreadybeen confirmed by the ALMA follow-up of the LABOCAsources in the LESS survey, as described by Hodge et al.(2013). Despite the significantly larger beam delivered bythe LABOCA imaging as compared to the SCUBA-2 imag-ing (approaching a factor of 2 in beam area, with thus signif-icantly increased likelihood of source multiplicity and blend-ing), 45 out of 57 of the robust IDs for LABOCA sources pro-posed by Biggs et al. (2011) were confirmed by the ALMAimaging (Hodge et al. 2013), yielding a reliability of ≃ 80per cent, and the correct position of the brightest ALMAcomponent was correctly predicted by the radio ID for 52
out of 69 LABOCA sources, yielding a completeness of ≃ 75per cent. This is higher than the completeness quoted byHodge et al. (2013), but they included all ALMA sources,not just the brightest ones for each LABOCA source. Ourapproach gives the fraction of single-dish sources for whichthe main component was correctly identified.
4.2 Multiplicity and number counts
First with the IRAM PdB and the SMA, and more recentlywith ALMA, it has now become possible to address the is-sue of the extent to which the submm galaxies detected bysingle-dish surveys consist of blends of fainter submm galax-ies lying within the single-dish primary beam (Wang et al.2011; Hodge et al. 2013; Karim et al. 2013; Simpson et al.2015b). Most of these studies reported a very high (> 50%)multiplicity rate, but this was based on including even thefaintest submm companions in the statistics, and in severalcases the single-dish beamsize was also significantly largerthan delivered by the JCMT at 850µm. In what follows werevise these numbers by treating as multiple only the caseswhen the secondary companion is sufficiently bright to po-tentially affect the identifications if only single-dish obser-vations were available. The impact of real physical associ-ations, or simply the blending of projected sources (i.e. atvery different redshifts) on single-dish flux densities and de-rived number counts is obviously a function of the size (i.e.FWHM) of the single dish primary beam, and hence is moreserious for surveys conducted at longer wavelengths, or withsmaller telescopes.
In the LABOCA Extended Chandra Deep Field Southsubmm survey (LESS; Weiß et al. 2009b), 20 out of 69LABOCA submm sources (≃ 30 per cent) were revealedby ALMA follow-up imaging to comprise multiple ALMAsources with a flux-density ratio < 3, leading to sugges-tions that source multiplicity might be a serious problemfor previous single-dish submm surveys (Hodge et al. 2013;Karim et al. 2013) (a flux-density ratio threshold of 3 isusually adopted as a minor/major merger threshold, e.g.Lambas et al. 2012; and also provides a reasonable thresh-old for considering which sources have had their single-dishflux-densities and positions seriously affected by source mul-tiplicity/blending). A slightly smaller fraction (16 of these69, ≡ 23 per cent) of these sources were also found to havemultiple radio IDs (Biggs et al. 2011).
However, the beam area of APEX/LABOCA is nearlytwice as large as that of JCMT/SCUBA-2, so the impactof source multiplicity on the SCUBA-2 results is expectedto be significantly smaller. This is confirmed by the ALMAfollow-up of the SCUBA-2 sources by Simpson et al. (2015b)as already presented in Fig. 2. Here only 6 out 30 (≃ 20 percent) of the SCUBA-2 sources have been found to consist ofmultiple ALMA sources with a flux-density ratio < 3, andarguably this is an overestimate for the full SCUBA-2 sourcesample, given that the sample studied by Simpson et al.(2015b) is biased towards brighter sources where blendingis likely to be a more serious issue (due to the steep slope atthe bright end of the submm luminosity function). Indeed, asis evident from Fig. 2, while the brightest SCUBA-2 sourceis clearly revealed to be a blend of two ALMA componentswith comparable flux densities, the majority of SCUBA-2source flux densities are well matched by the flux densities
of the brighter ALMA components, and it is clear that inmost cases the secondary ALMA component is either toofaint, or too well-separated from the brighter componentto significantly contaminate/bias the SCUBA-2 derived fluxdensity.
We can also explore the issue of multiplicity from a ra-dio perspective, by considering the prevalence of multipleradio IDs within the SCUBA-2 sample. If a submm sourceis composed of two or more sources with similar luminosi-ties at similar redshifts, then if the primary component isdetected in the radio with high signal-to-noise ratio, thenthe secondary component should also be detected. However,in the COSMOS (UDS) field, out of 181 (332) SCUBA-2sources with radio IDs, only 14 (26) have multiple radioIDs, i.e. ≃ 8 per cent (8 per cent). For 14 (18) of them thesecondary ID is also robust. The corresponding numbers formultiple 24-µm IDs are 7 per cent, or 27/401, 8 with robustsecondary IDs (6 per cent or 33/587, 5 with robust secondaryIDs). Finally, 9 per cent or 30/326, 5 with robust secondaryIDs (10 per cent or 45/452, 6 with robust secondary IDs) of8-µm IDs are multiple. However, the true multiplicity rateis likely higher, because unidentified sources could also rep-resent blends of fainter submm sources. Hence, to betterassess the ID multiplicity rate, we confined our attention toa subsample with high radio ID completeness. Among thetwenty > 10σ SCUBA-2 sources in the UDS field 17 (85 percent) have radio IDs and only 2/17 of these (i.e. 12 per cent)are multiple. An upper limit on multiplicity can be derivedby assuming that all sources lacking a radio are multiple,yielding (2 + 3)/20 (i.e. 25 per cent).
We conclude that, within our SCUBA-2 sample, only≃ 15–20 per cent of sources are potentially significantly af-fected by multiplicity and blending. Moreover, as we showin Fig. 3, the impact of any multiplicity/blending on thebright-end of the 850-µm source counts as derived fromSCUBA and SCUBA-2 surveys with the JCMT is verymodest. This shows that, even without any correction, theSCUBA-2 and ALMA flux-density distributions brightwardsof S850 ≃ 4 mJy are not significantly different (applicationof the Kolmogorov-Smirnov test yields a probability of only50 per cent that the ALMA and SCUBA-2 flux densities arenot drawn from the same parent population), and that ap-plication of a modest correction, either subtracting ≃ 1 mJyfrom all SCUBA-2 flux densities or, as shown in Fig. 3, mul-tiplying the SCUBA-2 flux densities by 0.9, is sufficient tobring the SCUBA-2 and ALMA bright source counts intonear perfect agreement. Our findings on the small impact ofmultiplicity on number counts are in agreement with thoseof Chen et al. (2013), in which they found only ∼ 15% oftheir SMA-targeted SCUBA-2 submm sources are multiples,and therefore their SCUBA-2 counts are not significantlyaffected by multiplicity either. Previous claims that submmnumber counts have been severely biased by source blend-ing appear to have been exaggerated, and in any case havegenerally been based on samples derived from imaging sur-veys with much larger beam sizes than are provided by theJCMT at 850 µm (Karim et al. 2013).
To summarise, given the success of our ID procedure inlocating the positions of the brightest ALMA components,the relatively low prevalence of significant ALMA subcom-ponents or secondary radio IDs, and the modest impact ofsource multiplicity on the bright end of the 850-µm source
counts, it is clear that source multiplicity and blending isnot a serious issue for the study of bright 850-µm sourcesselected at the angular resolution provided by the JCMT.
5 LONG-WAVELENGTH PHOTOMETRY
We now return to the issue of completing the redshift con-tent of the SCUBA-2 sample, and determining the physicalproperties of the sources. Because ≃ 25 per cent of even the> 4σ SCUBA-2 sources remain unidentified at optical/near-IR/mid-IR/radio wavelengths, and because some of the opti-cal identifications may be wrong (either because they are notstatistically robust, or because they are intervening lenses) itis crucial to utilise the available far-infrared and mm imag-ing available in the field to enable at least crude constraintson redshift to be established (by fitting to the anticipatedrest-frame far-infrared SED of the dust emission). This in-formation is also important, even for the identified sources,for estimating the dust-enshrouded SFR of each object.
We therefore used the Herschel4 (Pilbratt et al. 2010)Multi-tiered Extragalactic Survey (HerMES; Oliver et al.2012; Levenson et al. 2010; Viero et al. 2013) and thePACS Evolutionary Probe (PEP; Lutz et al. 2011) data ob-tained with the Spectral and Photometric Imaging Receiver(SPIRE; Griffin et al. 2010) and the Photodetector ArrayCamera and Spectrometer (PACS; Poglitsch et al. 2010),covering the entire COSMOS and UDS fields. We used mapsat 100, 160, 250, 350 and 500µm with beam sizes of 7.4, 11.3,18.2, 24.9 and 36.3 arcsec. The maps are available throughthe Herschel Database in Marseille (HeDaM)5 and the PEPwebsite6.
In addition, in order to constrain the long-wavelengthside of the SEDs of SCUBA-2 sources, we used the 1.1 mmAzTEC imaging data available in both survey fields. Thisimaging unfortunately does not cover all of the area sur-veyed with SCUBA-2, and is less deep than is desir-able, but nevertheless is provides detections for some ofour 850µm-selected galaxies, and useful upper limits fora significant fraction of the remainder. For the COSMOSfield we used the JCMT and ASTE AzTEC (Wilson et al.2008) maps and catalogues from Scott et al. (2008), andAretxaga et al. (2011), covering 0.15 and 0.72 deg2 down toan rms of 1.3 and 1.26 mJy beam−1, respectively. For theUDS field we used the JCMT and ASTE AzTEC data fromAustermann et al. (2010) and Kohno (private communica-tion). These cover 0.7 and 0.27 deg2 to an rms depth of 1.0–1.7 and 0.5 mJy beam−1, respectively.
We obtained the Herschel fluxes of each SCUBA-2source in the following way. We extracted 120-arcsec widestamps from all five Herschel maps around the position ofeach SCUBA-2 source. Then we processed the PACS (100and 160µm) maps by simultaneously fitting Gaussians withthe FWHM of the respective maps, centred at the positionsof all radio and 24-µm sources located within these cut-outs,
4 Herschel is an ESA space observatory with science instrumentsprovided by European-led Principal Investigator consortia andwith important participation from NASA.5 hedam.lam.fr6 www.mpe.mpg.de/ir/Research/PEP/DR1
and at the positions of the SCUBA-2 IDs. Then, to decon-volve the SPIRE (250, 350 and 500µm) maps in a similarway, we used the positions of the 24-µm sources detectedwith PACS (> 3σ), the positions of all radio sources, and theSCUBA-2 ID positions (or the submm positions if no radioor mid-IR ID had been secured). The errors were computedfrom the covariance matrix of the fit, in which the free pa-rameters are simply the heights of the Gaussian beams fittedat each input position. Then the confusion noise of 5.8, 6.3and 6.8 mJy beam−1 at 250, 350 and 500µm, respectively(Nguyen et al. 2010) was added in quadrature. The fittingwas performed using the IDL Mpfit
7 package (Markwardt2009).
To incorporate the information from the AzTEC imag-ing, we matched the SCUBA-2 and 1.1 mm catalogues within12 arcsec (the approximate sum in quadrature of the po-sitional uncertainties of SCUBA-2 and AzTEC sources),which resulted in 72 matches in the COSMOS field, and 118matches in the UDS field. Then we estimated the 1.1 mmfluxes for the non-matched SCUBA-2 sources in the sameway as for the Herschel fluxes. This was possible for an ad-ditional 211 SCUBA-2 sources in the COSMOS field and250 SCUBA-2 sources in the UDS field.
The derived long-wavelength fluxes are presented in Ta-bles A3 and A4 in the appendix.
Because the 1.1-mm information proves to be crucial forsetting meaningful upper bounds on the ‘long-wavelength’redshift estimates (particularly for SCUBA-2 sources withweak, or non-existent Herschel detections), we have re-stricted the remainder of the analysis presented in this paperto the subset of 651 (out of 1121) > 4σ SCUBA-2 sourcesfor which the AzTEC 1.1-mm coverage is available (283 inthe COSMOS field and 368 in the UDS field).
6 REDSHIFTS AND NUMBER DENSITY
We used the optical, near-IR and IRAC data (presentedin Tables A5 and A6 in the appendix) to fit the SEDs ofall IDs and to derive their photometric redshifts and phys-ical properties using the method of Cirasuolo et al. (2007,2010). This uses a modification of the HyperZ package(Bolzonella et al. 2000) with the stellar population mod-els of Bruzual & Charlot (2003) and a Chabrier (2003) ini-tial mass function (IMF) with a mass range 0.1–100 M⊙. Adouble-burst star formation history was assumed, but thischoice has little impact on derived redshifts (as opposed toderived stellar masses, which are well reproduced by thetwo-component star formation history for submm galaxies;Micha lowski et al. 2012a, 2014b). The metallicity was fixedat the solar value and reddening was calculated following theCalzetti et al. (2000) law within the range 0 6 AV 6 6 (seeDunlop et al. 2007). The age of the young stellar componentwas varied between 50 Myr and 1.5 Gyr, and the old compo-nent was allowed to contribute 0–100 per cent of the near-IRemission while its age was varied over the range 1–6 Gyr. TheHI absorption along the line of sight was included accord-ing to the prescription of Madau (1995). The accuracy of the
7 purl.com/net/mpfit
0 1 2 3 4 5 6Optical redshift
0
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6
Long
-wav
elen
gth
reds
hift
COSMOSUDS
zLW = zopt2sigma cut
Figure 4. Long-wavelength photometric redshift as a functionof optical/near-IR photometric redshift (Section 6) for the > 4σSCUBA-2 sources in the COSMOS (red) and UDS (blue) fieldsthat have 1.1mm coverage and optical/near-IR galaxy counter-parts. The solid line represent zLW = zopt, whereas the dashedlines show the 2σ cut from the Gaussian fit presented in Fig. 5.Thinner symbols (above the upper dashed line) represent objectsfor which the long-wavelength redshifts were adopted (see Sec-tion 6). The concentration of points at zLW = 3.9 is due toHerschel-undetected objects for which the minimum χ2 yieldedby the long-wavelength fitting is almost flat above some lowestpermitted value, and the formal best-fitting solution is at thatlowest allowed redshift.
photometric catalogue of Cirasuolo et al. (2010) is excellent,with a mean |zphot − zspec|/(1 + zspec) = 0.008 ± 0.034.
We also estimated ‘long-wavelength’ redshifts, as inKoprowski et al. (2014, 2016), fitting the average submmgalaxy template (from Micha lowski et al. 2010a) to thefar-IR and (sub)millimetre photometry (Herschel PACS,SPIRE, SCUBA-2 and AzTEC data). Non-detections weretreated in the same way as detections in the fitting, usingthe flux and error measured at a given position. Hence, thecase of Herschel non-detections resulted in ruling out low-zsolution and flat χ2 distributions at higher redshifts. Long-wavelength redshifts were especially useful for sources withno optical counterparts (or no IDs at all). This redshift de-termination is obviously not as accurate as the optical pho-tometric method, but provides an important estimate of the∆z ≃ 0.5-wide redshift bin within which a given source re-sides. For sources with optical/near-IR redshifts the median|zLW−zopt|/(1+zopt) for the COSMOS field is ≃ 0.16±0.03,while for the UDS field it is ≃ 0.011 ± 0.016. This is similarto the accuracy reported in Aretxaga et al. (2005, 2007).
For both redshift estimates the errors were calculatedby the determination of the redshift range over which χ2
increases by 1 from the minimum value while allowing allother parameters to vary.
The resulting redshifts are given in Tables A7 and A8 inthe appendix. For sources with multiple IDs, the ID with thesmallest p-value was used. The fraction of SCUBA-2 sourceswith optical/near-IR redshifts is summarised in Table 2 (col-umn 16). We obtained optical/near-IR photometric redshiftestimates for ≃ 60 per cent of the SCUBA-2 sources located
Table 3. Median properties of > 4σ SCUBA2 sources with 1.1mm coverage.
Field z zopt SFR SFRzopt log(Mstar/M⊙) sSFR fold(M⊙ yr−1) (M⊙ yr−1) (Gyr−1)
(1) (2) (3) (4) (5) (6) (7) (8)
COSMOS 2.40+0.11−0.05 2.11+0.04
−0.14 324+8−10 301+5
−14 11.11+0.05−0.04 2.12+0.13
−0.17 0.75+0.08−0.03
UDS 2.42+0.17−0.06 2.24+0.03
−0.04 261+6−6 258+6
−6 11.13+0.02−0.01 1.97+0.14
−0.11 0.93+0.01−0.01
Both 2.40+0.10−0.04 2.17+0.04
−0.04 287+7−6 269+13
−7 11.12+0.02−0.02 2.02+0.10
−0.08 0.91+0.01−0.02
(1) Field name; (2) Median redshift including all sources; (3) Median optical photometric redshift; (4) Median SFR including allobjects (using long-wavelength redshifts if optical redshifts are not available); (5) Median SFR including only objects with opticalphotometric redshifts; (6) Median stellar mass; (7) Median specific SFR. (8) Fraction of stellar mass contributed by the old stellar
component (see Section 6). For all properties but redshift only objects at z > 1 were taken into account.
0.00
0.05
0.10
0.15
Fra
ctio
n pe
r bi
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Both fieldsCOSMOSUDSGaussian fit2sigma cut
-1 0 1 2 3 4 5 6(zLW-zopt)/(1+zopt)
0.0
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N[<
(zLW
-zop
t)/(
1+zo
pt)]
Figure 5. The distribution (upper panel) and cumulative dis-tribution (lower panel) of the difference between the long-wavelength and optical/near-IR photometric redshifts (Section 6)
for the > 4σ SCUBA-2 sources that have 1.1mm coverage andoptical/near-IR galaxy counterparts. The solid curves are colour-coded depending on the field. The dotted line is a Gaussian fitto the negative side of distribution (with σ = 0.23), whereas thedashed line is the 2σ cut of this Gaussian, above which the opticalredshifts are deemed incorrect due to poorly determined redshifts,incorrect identifications, or because the optical counterpart is alikely a foreground galaxy lens.
inside the deep optical/near-IR imaging maps. The remain-ing ≃ 40 per cent either do not have IDs at all, or no opticalsource was matched to the radio/mid-IR IDs.
For 50 IDs in the COSMOS field and 20 in the UDSspectroscopic redshifts (Section 2.3) were available and usedinstead of optical photometric redshifts.
As in Koprowski et al. (2016), we attempted to filter theoptical/near-IR redshifts, replacing these redshift estimateswith the long-wavelength photometric redshift values whenthe two values are formally inconsistent. In practice, wherethe two values differ dramatically, it is in the sense that theoptical/near-IR photometric redshift estimate is too low, ei-ther because the optical counterpart has been assigned inerror, or because the identified optical galaxy is in fact lens-ing a more distant submm source (as in Negrello et al. 2010).In Fig. 4 we show the long-wavelength redshift as a functionof optical redshift, and in Fig. 5 we show the distributionof the difference between the long-wavelength and opticalredshifts, (zLW − zopt)/(1 + zopt).
We fitted a Gaussian to the negative side of the distri-bution obtaining a width of σ = 0.23. Then we discardedoptical/near-IR photometric redshifts (and the correspond-ing IDs) for sources with long-wavelength redshifts that are2σ higher (above the dashed lines in Figs 4 and 5), and there-after retain only the long-wavelength redshift estimates forthese sources. This happened for 23 robust and 14 tentativeprimary IDs in the COSMOS field and 42 robust and 11tentative primary IDs in the UDS field. Out of 651 > 4σsources with 1.1 mm coverage 349 have optical counterpartsretained in the analysis because of the consistency with thelong-wavelength redshift (160 in the COSMOS field and 189in the UDS field).
The substantial scatter in the zLW versus zopt plot(Fig. 4) can be fully explained by photometry measurementerrors. The median contribution of the data points to the χ2
with respect to the zLW = zopt line is ∼ 1.5, so this modelexplains the data reasonably well. This justifies our choice ofa single template in deriving zLW, as the data do not requirea more complex model.
The resulting final redshift distribution of the SCUBA-2sources is shown in Fig. 6, sub-divided by the type of red-shift calculation (all, optical/near-IR, long-wavelength), bythe survey field, and by the quality of the ID. The medianredshift for the full > 4σ SCUBA-2 sample with 1.1-mmcoverage is z = 2.40+0.10
−0.04 for all sources, or z = 2.17 ± 0.04for the subset of sources with retained optical/near-IR red-shifts (see Table 3), consistent with previous studies of
Figure 6. Top: the redshift distribution of the > 4σ SCUBA-2 sources that have 1.1mm coverage showing all sources (blacksolid line), those with optical/near-IR redshifts (blue dashed line),and those with long-wavelength redshifts only (red dotted line).Middle: the redshift distribution divided by field. The line typeis the same as in the top panel: solid lines denote all redshifts,and dashed lines denote optical/near-IR redshifts. The lines arecolour-coded by the field: black: both fields, red: COSMOS, blue:UDS. Bottom: the redshift distribution divided by the quality ofIDs. The black solid line is the same as above, whereas the bluedashed line denotes robust IDs (p 6 0.05), and the red dotted linedenotes tentative IDs (0.05 < p 6 0.1).
smaller samples of submm galaxies (Chapman et al. 2005;Chapin et al. 2009; Wardlow et al. 2011; Micha lowski et al.2012b; Yun et al. 2012; Simpson et al. 2014; Chen et al.2016; Koprowski et al. 2016).
While the median redshifts are consistent with previousstudies, our large sample size, and the use of long-wavelengthphotometric redshifts to complete the redshift content of thesample, has enabled us to more clearly reveal/define the ex-tent of the high-redshift tail of the submm galaxy popula-tion. Obviously, sources with no optical/near-IR redshifts(red dotted histogram on the top panel of Fig. 6) have, onaverage, higher redshifts than the remaining sample. Takinginto account both optical/near-IR and long-wavelength red-shifts, as much as 393 out of 1691 (23 per cent) SCUBA-2sources are at z > 4. However, only 39 sources (10 in COS-MOS and 29 in UDS field) have optical/near-IR z > 4. Sim-ilarly, out of 651 > 4σ SCUBA-2 sources with 1.1 mm cover-age, 93 (14 per cent) are at z > 4 and 19 have optical/near-IR z > 4 (6 in COSMOS and 13 in the UDS field).
5 10 15 20F850 / mJy
0
1
2
3
4
5
6
Red
shift
COSMOSUDSzoptzLWKoprowski16Medians in flux bins
90% value
Figure 7. Redshift as a function of 850 µm flux density for theSCUBA-2 sources in the COSMOS (red) and UDS (blue) fields,and in the deeper data in the COSMOS field (green crosses) pre-sented in Koprowski et al. (2016). Again we plot only the > 4σSCUBA-2 sources that have 1.1mm coverage. Larger symbolscorrespond to sources with optical/near-IR redshifts, whereassmaller symbols indicate those with only long-wavelength red-shifts. The black circles with error bars correspond to mediansin flux bins indicated by horizontal error bars. The dashed lineshows the redshift value above which 90 per cent of objects in agiven flux-density bin are located. The correlation of flux and red-shift is significant, as the Spearman’s rank correlation coefficientis 0.19 with a very small probability (∼ 3 × 10−7) that the nullhypothesis (no correlation) is correct. Sources at z = 6 have onlyHerschel upper limits, so while the formal best solution is at themaximum allowed redshift, the error bars are large, and extendto much lower redshifts. On the other hand, the concentrationof points at zLW = 3.9 is due to Herschel-undetected objects forwhich the redshift-dependence of minimum χ2 is nearly flat abovesome minimum permitted value, and the formal best solution isat that lowest redshift.
The middle panel of Fig. 6 shows that the redshift distri-butions in the COSMOS and UDS fields separately (both us-ing all redshifts and only optical redshifts) are qualitativelysimilar, displaying a peak at z ≃ 2. This means that with≃ 1 deg2 fields we start to overcome the cosmic variance,which makes number counts (Scott et al. 2010, 2012) andredshift distributions (Micha lowski et al. 2012b) derived us-ing smaller fields significantly different from each other. Ap-plication of the Kolmogorov-Smirnov test results in proba-bility of ≃ 0.7 per cent that the COSMOS and UDS samplesare drawn from the same parent population, but this is a. 3σ discrepancy.
The redshift distribution of tentative IDs (0.05 < p 6
0.1, red dotted histogram on the bottom panel of Fig. 6) isnot shifted towards lower redshifts with respect to robustIDs (p 6 0.05, blue dashed line), as would be expected iftentative IDs were significantly contaminated by unrelatedgalaxies (because lower-redshift galaxies dominate opticalcatalogues). In any case, the fraction of tentative IDs isonly ≃ 15 per cent (Table 2; both before and after long-wavelength redshift filtering), so they do not significantlyaffect our conclusions.
Figure 8. The comoving number density as a function of red-shift of submm galaxies with SFR > 300M⊙ yr−1 (our survey issensitive to such objects at all redshifts, see Fig. 9).
galaxies with higher fluxes are located at preferentiallyat higher redshifts (Ivison et al. 2002; Pope et al. 2005;Micha lowski et al. 2012b; Koprowski et al. 2014), and withour large sample we are able to further investigate this is-sue. Fig. 7 shows submm flux as a function of redshift for theSCUBA-2 sources presented here and in a deeper SCUBA-2image in the COSMOS field (Koprowski et al. 2016). It isevident that the bottom-right corner of this plot (high fluxdensity, low redshift) is empty, and this is not due to se-lection effects, as such sources should be easy to detect atall wavelengths, and redshifts easy to measure. The scat-ter in this figure is large but a weak overall trend can bediscerned. The Spearman rank correlation coefficient is 0.19with a very small probability (∼ 3 × 10−7) of the null hy-pothesis (no correlation) being acceptable. However, there isno real evidence for a deficit of lower luminosity objects athigh-redshift, and so this statistically significant correlationis driven by the absence of submm bright low-redshift ob-jects; very luminous submm galaxies are only found in oursurvey at z > 2.
In Fig. 8 we utilise the redshift content of our SCUBA-2 sample to plot the comoving number density of submmgalaxies with SFR > 300 M⊙ yr−1 as function of redshift.The values are shown in Table 4. Our survey is sensitive tosuch objects at all redshifts (see next section, and Fig. 9), sothis figure shows an unbiased and complete estimate of thecosmological evolution of the number density of the most lu-minous star forming galaxies in the Universe. It can be seenthat, although such objects are largely confined to z > 2,their number density declines significantly beyond z ≃ 3.5.Nevertheless, they still appear to persist at number densitiessignificantly in excess of 10−6 Mpc−3 at z ≃ 5.
7 STAR FORMATION RATES AND STELLAR
MASSES
We estimated SFRs from the fits of the average submmgalaxy template (Micha lowski et al. 2010a, dust tempera-ture ∼ 39 K) to the > 100µm photometry assuming ei-
ther the optical redshift if available, or the long-wavelengthredshift (Section 6). We integrated the template between 8and1000µm and applied the Kennicutt (1998) conversionscaled to the Chabrier (2003) IMF: SFR = 10−10 ×LIR/L⊙.Our data sample the peak of the dust SED, so if we useda hotter SED template (Arp 220; Silva et al. 1998), thenthe obtained SFRs would be only ∼ 20–30 per cent higher,within the systematic uncertainty of these estimates. For ob-jects with optical counterparts we estimated stellar massesfrom the optical/near-IR SED fits (Section 6).
The resulting SFRs, stellar masses are given in TablesA7 and A8 in the appendix. The SFRs, stellar masses andsSFRs are shown as a function of redshift in Fig. 9. Ta-ble 3 shows median values of these estimates for sources atz > 1 (excluding discarded optical redshifts, see Section 6).Fig. 10 shows the SFRs as a function of stellar mass, incomparison with the main sequence of star-forming galaxies(Speagle et al. 2014).
The second panel of Fig. 9 shows that SCUBA-2 sourcesare very massive galaxies with median masses of 1011.15 M⊙.in this figure we also show our stellar mass sensitivitylimit derived from the K-band detection limit (Table 1) k-corrected to the rest-frame K-band luminosity using the av-erage submm galaxy template of Micha lowski et al. (2010a)and using the mass-to-light ratio Mstar/LK = 0.3 M⊙L⊙
−1.Most of the SCUBA-2 sources are above these limits byan order of magnitude, so our optical/near-IR data is deepenough to ensure the detection of the overwhelming major-ity of the optical/near-IR counterparts. Hence, our medianmass estimate is not biased towards a high value, nor oursSFR estimate is biased towards a low value. These highstellar masses are not directly a result of high SFRs, be-cause, in most cases, ≃ 90 per cent of the stellar mass wasformed before the currently observed star-formation activity(column 8 of Table 3). This is consistent with the findingsof Dye et al. (2008) and Micha lowski et al. (2010a, 2012a).When modelled, as here, by a single burst, the mean age ofearlier star formation is ≃ 1–1.5 Gyr prior to the epoch of ob-servation. However, we caution that this does not mean thatthe mass-dominant component was formed in an earlier evenmore violent short-lived starburst event. Instead, the ∼90per cent of the pre-existing mass could have formed in an ex-tended (several Gyr) period, and indeed could have formedin smaller subcomponents, which subsequently merged.
The lower panels of Fig. 9 and Fig. 10 show that theSCUBA-2 sources at z > 2 (where most of them reside)are fully consistent with the main sequence of star-forminggalaxies (as quantified by Speagle et al. 2014) and form itshigh-mass end. This is especially highlighted in the fourthpanel of this figure, which limits the sample to those withMstar > 1011 M⊙ and SFR > 300 M⊙ yr−1, as our surveyis sensitive to such objects even at z ≃ 5. In this panel themedians of sSFRs in redshift bins are constant at z = 1–6and, given the behaviour of the mean sSFR of other galaxies,SCUBA-2 sources stay on the main sequence above z = 1.5.This is also true for SCUBA-2 sources at z > 4. This isthe first time that a significant sample of submm galaxies atsuch high redshifts has been studied in relation to the mainsequence.
Even at 1 < z < 2 most of the SCUBA-2 sources lie onor close to the main sequence, offset by less than a factor of2. Only at z < 1 do the SCUBA-2 sources lie significantly
Figure 10. Star formation rates as a function of stellar mass for the SCUBA-2 sources in the COSMOS (circles) and UDS (squares)fields. The solid lines represent the main sequence of star-forming galaxies at various redshifts, as reported by Speagle et al. (2014).Most submm galaxies lie on the main sequence. Dots represent the synthetic main-sequence galaxies distributed according to the massfunction of Ilbert et al. (2013) and Grazian et al. (2015) and the main sequence reported by Speagle et al. (2014), see Section 7. Theirnumber above the submm galaxy SFR threshold (dashed line), corresponding to 3.5mJy, is similar or larger than the number of realsubmm galaxies (corrected for completeness), which implies that submm galaxies can be fully explained as the most massive and mosthighly star-forming main-sequence galaxies, and hence they should not be regarded as a distinct starburst population. We note that theapparent asymmetry in the distribution of synthetic galaxies (enhancement above the main sequence) is an optical illusion. This can beverified by looking at a narrow mass range, which then shows a perfect symmetry.
Figure 11. Star formation rates as a function of stellar mass for the synthetic galaxies shown on Fig. 10. Those with star formationrates placing them above our 850µm survey limit of 3.5mJy are marked as crosses.
above the main sequence, and correspond to starburst galax-ies. At these redshifts our submm flux limit corresponds to alower luminosity than that at higher redshifts, but the main-sequence normalisation declines even faster from z ≃ 2 toz < 1.
Finally, we note that that, for sources that are in factblends of several sources (Section 4), our SFRs are overes-timated, as they include the contribution of other sources,whereas the stellar masses are correct, as long as we identifythe correct main contributor to the submm flux. Hence, the
true sSFR for these sources are even lower, which makes ourconclusion stronger that most of submm galaxies are notabove the main sequence.
In order to test whether submm galaxies can indeedbe almost exclusively main-sequence galaxies, we consideredhow many massive main-sequence galaxies with high SFRsare expected to be located in our ≃ 2.18 deg2 fields, givenwhat we know about the galaxy stellar mass function andSFRs of star-forming galaxies at a given redshift. To esti-mate the expected number density of such objects we usedthe mass function of Ilbert et al. (2013) at z < 4 and ofGrazian et al. (2015) at z > 4. For each redshift bin shownin Fig. 10 we multiplied the integral of the correspondingmass function between log(Mstar/M⊙) = 10–12 (the rangespanned by submm galaxies) with the volume probed by oursurvey within this redshift bin to obtain the total numberof star-forming galaxies in this mass range expected in ourfields. Their masses were chosen randomly out of the massfunction, so that the resulting mass distribution matches themeasured mass function. To each of these synthetic galax-ies we assigned an SFR based on the main sequence at thatredshift (Speagle et al. 2014) and scattered them randomlyby a number drawn from a Gaussian distribution with astandard deviation of 0.2 dex (the width of the main se-quence; Speagle et al. 2014). These synthetic main-sequencegalaxies are shown as dots in Fig. 10 and the number ofthem above the SFR cut corresponding to submm galaxies(dashed line) is shown on each panel as ‘Nsim’ (these most-star-forming synthetic galaxies are clearly marked as plussigns in Fig. 11).
Between z ≃ 1 and z ≃ 4 the number of predicted andobserved bright submm galaxies is in very good agreement(to within a factor of 2) given the relative simplicity of thiscalculation. Indeed the predicted number is always largerthat what is actually observed, particularly so at z > 4, andso given current data on the evolution of the galaxy massfunction and the main sequence, there is clearly no problemexplaining the prevalence of submm galaxies at all redshifts.
There are some obvious reasons that this calculationmay overpredict somewhat the observed number of submmgalaxies at the highest redshifts. Given the small numberstatistics at z > 4 redshift errors may be important, and inaddition our completeness may be poorer than estimated.However, it is equally likely that the predicted number ofmassive star-forming galaxies may be in error at the highestredshifts, given our current limited knowledge of the formof the galaxy stellar mass function at z > 4 (the high-massend being particularly vulnerable to systematic errors suchas Eddington bias).
Nonetheless, these calculations, as illustrated in Fig. 10and Fig. 11, clearly demonstrate that the observed proper-ties of luminous high-redshift submm galaxies arise naturallyfrom the evolving main sequence of normal star-forming ob-jects, once the selection function inherent in submm surveysis taken into account.
8 DISCUSSION: EXTREME STAR
FORMATION IN THE UNIVERSE
8.1 Main-sequence nature and the maximum SFR
Koprowski et al. (2016) showed that submm galaxies in deep
0 1 2 3 4 5 6Redshift
0.0001
0.0010
0.0100
0.1000
SF
R d
ensi
ty /
MO • y
r-1 M
pc-3
SFR > 300 MO • yr-1
Figure 12. The comoving star formation rate density contributedby submm galaxies with SFR > 300M⊙ yr−1 (squares; our surveyis sensitive to such objects at all redshifts, see Fig. 9). The Solidline indicates a recent determination of the total SFR density(Madau & Dickinson 2014).
0 1 2 3 4 5 6Redshift
105
106
107
108
109
Ste
llar
mas
s de
nsity
/ M
O • M
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Figure 13. The comoving stellar mass density contributed bysubmm galaxies with SFR > 300M⊙ yr−1 (squares; our sur-vey is sensitive to such objects at all redshifts, see Fig. 9).Grey points represent the total stellar mass density compiled inMadau & Dickinson (2014) .
SCUBA-2 fields are located on the main sequence, and nowwe have obtained a similar result for a brighter sample fromshallower but larger fields. This is incompatible with thefrequently assumed picture that submm galaxies are unusu-ally powerful starbursts, significantly different from the gen-eral star-forming galaxy population. Instead, we have shownthat submm surveys simply (and inevitably) select the mostmassive (and hence most star-forming) galaxies out of themain-sequence population. This suggests that most submmgalaxies are not fuelled by extreme, transitory event such asa major merger (which would move them above the main se-quence), but instead represent the final stages (shortly priorto quenching) of a long and (on average) fairly smooth, as-
Table 4. Comoving number density, SFR density, and stellar mass density of submm galaxies and fraction of galaxies withlog(Mstar/M⊙) > 11 which are submm galaxies based on our > 4σ sample with 1.1mm coverage.
Figure 14. The fraction of galaxies with masses abovelog(Mstar/M⊙) = 11 that are submm galaxies (squares), derivedfrom the comparison of the number density of submm galaxies
and the integral of the total mass function of star-forming galax-ies at a given redshift (Ilbert et al. 2013; Grazian et al. 2015;Caputi et al. 2015) above this stellar mass. The Solid line is apower-law fit of the form of (2.9 ± 0.4) × z1.56±0.16.
cent up the main sequence. This interpretation is supportedby recent simulations showing that all properties of submmgalaxies can be explained by a sustained gas inflow, ratherthan by major mergers (Narayanan et al. 2015).
It is clear however, that some submm galaxies are pow-ered by major mergers. CO and Hα observations revealedthat in roughly half of the submm galaxies gas is distributedin multiple components and in the remaining half the gasdistribution is compact (Tacconi et al. 2008; Engel et al.2010; Alaghband-Zadeh et al. 2012). This is consistent witha major merger scenario, but also with a clumpy discscenario if the separation is not too large. On the otherhand, near-IR (Targett et al. 2011, 2013; Wiklind et al.2014; Chen et al. 2015), kinematic (Swinbank et al. 2011;Hodge et al. 2012; Menendez-Delmestre et al. 2013) and re-solved dust/gas studies (Bothwell et al. 2010; Hodge et al.2015, 2016) of submm galaxies indeed reveals that some ofthem are large, clumpy disc galaxies, sometimes with poten-tial merger signatures.
Due to our large and well-defined bright SCUBA-2 sam-ple, this is the first time that it has proved possible to prop-erly investigate the position of submm galaxies at z > 4relative to the main sequence. As demonstrated in Figs 9and 10, even at such high redshifts, submm galaxies areconsistent on average with the main sequence. Hence, thesegalaxies represent the most powerful star-forming galaxiesat these early epochs, but again are likely not powered byany unusual/extreme events.
It is instructive to investigate whether there is a limitto the SFR of submm galaxies. We have not detected anysource above SFR = 1500 M⊙ yr−1 (dotted line on the toppanel of Fig. 9). This is because the sources in our sampledo not exceed the 850µm flux of 17 mJy. This implies anupper limit on the number density of such extreme sourcesof < 0.023 deg2 (95 per cent confidence). This was calcu-lated as 1/20 of a number density if there was one galaxyper 2.17 deg2 field (the area of the combined UDS and COS-MOS SCUBA-2 fields) and can be confirmed by generatingrandom positions in a large area (e.g. 100 deg2) and checkingthat, at this surface density, 95 per cent of random 2.17 deg2
fields contain no sources. Sources more active than SFR =1500 M⊙ yr−1 have been confirmed in the past (Capak et al.2008, 2011; Daddi et al. 2009b; Micha lowski et al. 2010b;Riechers et al. 2010, 2013; Hezaveh et al. 2013), but usuallythey were just single objects in given fields, so the estimateof their number density is difficult.
We note that our SFR cutoff value is higher than themaximum of 1000 M⊙ yr−1 proposed by Karim et al. (2013)based on the lack of > 9 mJy sources in the ALMA follow-upof LESS sources. However, their smaller parent single-dishsample contained only a few such sources, so the conclusionpresented here is more robust. Indeed, > 9 mJy interfer-ometric sources were detected by both the SubmillimeterArray (SMA; Younger et al. 2007, 2009; Barger et al. 2012)and ALMA (Simpson et al. 2015a,b). On the other handBarger et al. (2014) found a turn-down in the SFR distribu-tion function above ∼ 1100 M⊙ yr−1 (after the conversionto our adopted Chabrier (2003) IMF), which implies thatsuch sources become increasingly rare, which is compatiblewith our cut-off value.
We have not found any IDs for around a third of SCUBA-2 sources (Table 2). They can be divided into three cat-egories: (1) spurious or flux-boosted submm sources, (2)blends of several submm sources out of which none is brightenough for our ID method to work and (3) high redshiftsources, with too low radio and mid-IR fluxes. Our IDmethod is likely to miss spurious sources (they would bevery unlikely to yield IDs), and, as demonstrated in Fig. 2,would not identify many faint submm sources, especiallyat high redshift. Hence, our ID catalogue should reflect arelatively clean submm-flux-limited sample (removing prob-lematic categories 1 and 2), but may underrepresent veryhigh redshift sources, which are likely not to be IDed either.Indeed, the ID completeness of the entire sample (∼ 66 percent) is lower than the ID completeness in the ALMA sub-sample (∼ 86 per cent). This is likely because the ALMAsubsample is brighter (fig. 2 of Simpson et al. 2015b), so itcontains less spurious sources, for which our method would(correctly) return no ID.
We can estimate the fraction of sources in these cate-gories based on our ID fraction (Table 2) and the ALMAtraining sample (Simpson et al. 2015b, and Section 3). InSection 3 we showed that for four out of 29 ALMA-observedSCUBA-2 sources our ID method misses dominant ALMAsources. One of them is not covered by the 24µm imaging,but the lack of IDs for other three (∼ 10 per cent) indicatesthat they can be at very high redshifts. They are unlikely tobe spurious sources, as the fluxes are confirmed by ALMA atthe ∼ 4–8 mJy level. Two of them are not detected by Her-schel implying a long-wavelength redshift of > 3 and > 4(Section 6). The third has a significant Herschel signal, butthere are two very strong 24µm sources nearby complicatingthe photometry. In any case, some other SCUBA-2 sourceswith no IDs (for which we do not have ALMA data) mayalso belong to the high-z category. This can be tested byhigh-resolution submm interferometry and subsequent COredshift search.
Some sources can be affected by blending. For three outof 29 ALMA-observed SCUBA-2 sources (∼ 10 per cent)the brightest ALMA component is fainter than half of theSCUBA-2 flux. Additionally, for two SCUBA-2 sources thereare no ALMA counterparts. This means that for ∼ 17 percent of the SCUBA-2 sample the true submm flux may betwice lower than measured, making it difficult to find IDs.We also note that multiplicity should not influence our long-wavelength estimates, because if given sources are blendedat the JCMT/SCUBA-2 resolution, then they are blendedat the Herschel/SPIRE resolution. Hence, far-IR colours ofthe main contributor to the submm flux are not significantlyaffected, unless the sources are at significantly different red-shifts. Hence, sources with no IDs with zLW ∼ 2 are likelyblends of galaxies at that redshift, whereas those with noIDs and zLW & 4 are likely truly at these high redshifts, asblending should not result in an artificially high zLW.
8.3 Submm galaxies in cosmological context
Fig. 12 and Fig. 13 show the contribution of our submmgalaxies to the cosmic SFR and stellar mass densities, re-spectively. The values are shown in Table 4. To calculate the
volume of each redshift bin we assumed the combined areaof the COSMOS and UDS SCUBA-2 imaging with 1.1 mmcoverage of 1.15 deg2. For each source we used our best red-shift, either optical, or long-wavelength if optical was notavailable or rejected. Completeness corrections have beenapplied as described in Geach et al. (2017).
Bright submm galaxies, as studied here, contribute ≃ 2–4 per cent of the SFR density at z = 2–6, and ≃ 3 per centto the stellar mass density at z = 2–4, rising to ≃ 10 percent at z = 4–6. Deeper mm/submm surveys with SCUBA-2 (e.g. Casey et al. 2013; Barger et al. 2014; Coppin et al.2015; Bourne et al. 2017) and ALMA (Dunlop et al. 2017)show that fainter dusty star-forming galaxies contribute thevast majority of cosmic star formation rate density at z = 1–3.
Finally, in Fig. 14 and Table 4 we show the fraction ofstar-forming galaxies above log(Mstar/M⊙) = 11 that aresubmm galaxies, calculated by dividing the number densityof submm galaxies with log(Mstar/M⊙) > 11 in a given red-shift bin by the integral of the mass function (Ilbert et al.2013; Grazian et al. 2015; Caputi et al. 2015) above thatmass. The power-law fit to this fraction results in the fol-lowing dependence: (2.9 ± 0.4) × z1.56±0.16 . The fraction ofsubmm galaxies increases with redshift and reaches ≃ 30per cent at z = 4. This is because our selection functionis nearly flat with redshift, whereas the normalisation ofthe main sequence is increasing, so the fraction of massivegalaxies that should be detectable above our SFR-limitedflux-density limit is expected to increase (albeit the totalnumber density of such massive galaxies obviously rapidlydeclines with increasing redshift).
9 CONCLUSIONS
We have conducted an analysis of nearly 2000 submmsources detected in the ≃ 2 deg2 850-µm imaging of theCOSMOS and UDS fields obtained with SCUBA-2 on theJCMT as part of the SCUBA-2 Cosmology Legacy Survey.This unique data set represents the largest homogeneoussample of 850-µm-selected sources assembled to date, and wehave exploited this sample, along with the rich multiwave-length supporting data in these fields to shed new light onthe physical properties and cosmological evolution of bright(S850 > 4 mJy) submm-selected galaxies.
We have completed the galaxy identification processfor all 850-µm sources selected with S/N > 3.5, but fo-cus our scientific analysis on a high-quality subsample of651 sources selected with S/N > 4 and complete multiwave-length coverage extending to include 1.1-mm imaging. Wehave checked the reliability of our identifications, and the ro-bustness of the SCUBA-2 fluxes, by revisiting the results ofrecent ALMA follow-up of a subset of the brightest sourcesin our sample. This shows that our identification methodhas a completeness of ≃ 86 per cent with a reliability of≃ 92 per cent, and that only ≃ 15–20 per cent of sourcesare significantly affected by multiplicity. For completeness,we have also shown that the impact of source blending onthe 850-µm source counts as determined with SCUBA-2 ismodest; scaling the single-dish fluxes by ≃ 0.9 reproducesthe ALMA source counts.
The optical/near-IR/mid-IR data, coupled at longer
wavelengths with the Herschel+SCUBA-2+AzTEC pho-tometry, have enabled us to estimate the redshifts (z) andstar formation rates (SFR) of all sources in our entire sam-ple, and stellar masses (Mstar) for the ≃ 75 per cent ofsources with optical/near-IR galaxy identifications.
For our 4σ sample with 1.1 mm coverage we find me-dian values of z = 2.40+0.10
−0.04 , SFR = 287 ± 6 M⊙ yr−1 andlog(Mstar/M⊙) = 11.12±0.02 (the latter for 349/651 sourceswith optical identifications), and we have shown that theseproperties clearly locate bright submm galaxies on the high-mass end of the ‘main sequence’ of star-forming galaxies outto z ≃ 6, suggesting that major mergers are not a dominantdriver of the high-redshift submm-selected population. Wehave also shown that the number densities of these high-mass main-sequence galaxies are consistent with recent de-terminations of the evolving galaxy stellar mass function,and have calculated the contributions of these most lumi-nous star-forming main-sequence galaxies to cosmic star for-mation rate density and cosmic stellar mass density as afunction of redshift.
We conclude that the submm galaxy population is es-sentially as expected (both in terms of evolving comovingnumber density, and with regard to inferred physical proper-ties), albeit reproducing the evolution of the main sequenceof star-forming galaxies remains a challenge for theoreticalmodels/simulations.
ACKNOWLEDGEMENTS
We thank Joanna Baradziej, Ian Smail, James Simpson, andour anonymous referee for comments and suggestions.
MJM acknowledges the support of the UK Science andTechnology Facilities Council (STFC), British Council Re-searcher Links Travel Grant and the hospitality at the In-stituto Nacional de Astrofısica, Optica y Electronica. JSDacknowledges the support of the European Research Coun-cil via the award of an Advanced Grant, and the contri-bution of the EC FP7 SPACE project ASTRODEEP (Ref.No: 312725). MPK acknowledges the STFC Studentship En-hancement Programme grant and the Carnegie Trust Re-search Incentive Grant (PI: Micha lowski).
The James Clerk Maxwell Telescope has historicallybeen operated by the Joint Astronomy Centre on behalf ofthe Science and Technology Facilities Council of the UnitedKingdom, the National Research Council of Canada and theNetherlands Organisation for Scientific Research. Additionalfunds for the construction of SCUBA-2 were provided by theCanada Foundation for Innovation.
This work is based on data products from observationsmade with ESO Telescopes at the La Silla Paranal Observa-tory as part of programme ID 179.A-2005, using data prod-ucts produced by TERAPIX and the Cambridge AstronomySurvey Unit on behalf of the UltraVISTA consortium.
This study was based in part on observations obtainedwith MegaPrime/MegaCam, a joint project of CFHT andCEA/DAPNIA, at the Canada-France-Hawaii Telescope(CFHT) which is operated by the National Research Coun-cil (NRC) of Canada, the Institut National des Science del’Univers of the Centre National de la Recherche Scientifique(CNRS) of France, and the University of Hawaii. This workis based in part on data products produced at TERAPIX
and the Canadian Astronomy Data Centre as part of theCanada-France-Hawaii Telescope Legacy Survey, a collabo-rative project of NRC and CNRS.
The National Radio Astronomy Observatory is a facilityof the National Science Foundation operated under cooper-ative agreement by Associated Universities, Inc.
This work is based in part on observations made withthe Spitzer Space Telescope, which is operated by the JetPropulsion Laboratory, California Institute of Technologyunder a contract with NASA.
This research has made use of data from HerMESproject (http://hermes.sussex.ac.uk/). HerMES is a Her-schel Key Programme utilising Guaranteed Time from theSPIRE instrument team, ESAC scientists and a mission sci-entist. The HerMES data was accessed through the Her-schel Database in Marseille (HeDaM - http://hedam.lam.fr)operated by CeSAM and hosted by the Laboratoired’Astrophysique de Marseille. PACS has been developedby a consortium of institutes led by MPE (Germany) andincluding UVIE (Austria); KU Leuven, CSL, IMEC (Bel-gium); CEA, LAM (France); MPIA (Germany); INAF-IFSI/OAA/OAP/OAT, LENS, SISSA (Italy); IAC (Spain).This development has been supported by the fundingagencies BMVIT (Austria), ESA-PRODEX (Belgium),CEA/CNES (France), DLR (Germany), ASI/INAF (Italy),and CICYT/MCYT (Spain). SPIRE has been developedby a consortium of institutes led by Cardiff University(UK) and including Univ. Lethbridge (Canada); NAOC(China); CEA, LAM (France); IFSI, Univ. Padua (Italy);IAC (Spain); Stockholm Observatory (Sweden); ImperialCollege London, RAL, UCL-MSSL, UKATC, Univ. Sussex(UK); and Caltech, JPL, NHSC, Univ. Colorado (USA).This development has been supported by national fund-ing agencies: CSA (Canada); NAOC (China); CEA, CNES,CNRS (France); ASI (Italy); MCINN (Spain); SNSB (Swe-den); STFC (UK); and NASA (USA).
This work is based on observations taken by the3D-HST Treasury Program (GO 12177 and 12328) withthe NASA/ESA HST, which is operated by the Asso-ciation of Universities for Research in Astronomy, Inc.,under NASA contract NAS5-26555. Based on data ob-tained with the European Southern Observatory Very LargeTelescope, Paranal, Chile, under Large Program 185.A-0791, and made available by the VUDS team at the CE-SAM data center, Laboratoire d’Astrophysique de Mar-seille, France. The HST data matched to the VUDS-DR1are described in Grogin et al. (2011) and Koekemoer et al.(2011) for CANDELS and include data from the ERS(Windhorst et al. 2011). This paper uses data from theVIMOS Public Extragalactic Redshift Survey (VIPERS).VIPERS has been performed using the ESO Very LargeTelescope, under the ”Large Programme” 182.A-0886. Theparticipating institutions and funding agencies are listed athttp://vipers.inaf.it Based on zCOSMOS observationscarried out using the Very Large Telescope at the ESOParanal Observatory under Programme ID: LP175.A-0839
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Table A1: Radio, 24µm and 8 µm identifications of the JCMT/SCUBA2 objects in the COSMOSfield. This table is available in its entirety in the online version.
No. RA1.4 DEC1.4 F1.4 E1.4 Sep p RA24 DEC24 F24 E24 Sep p RA8 DEC8 F8 E8 Sep p
Table A2: Radio, 24 µm and 8µm identifications of the JCMT/SCUBA2 objects in the UDS field.This table is available in its entirety in the online version.
No. RA1.4 DEC1.4 F1.4 E1.4 Sep p RA24 DEC24 F24 E24 Sep p RA8 DEC8 F8 E8 Sep p
Table A7: Redshift and physical properties of the JCMT/SCUBA2 objects in the COSMOS field.This table is available in its entirety in the online version.
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