# Deep Extragalactic VIsible Legacy Survey (DEVILS): Consistent Multi-
wavelength Photometry for the DEVILS Regions (COSMOS, XMMLSS & ECDFS)

L. J. M. Davies1, J. E. Thorne1, A. S. G. Robotham1,2, S. Bellstedt1, S. P.
Driver1, N. J. Adams3, M. Bilicki4, R. A. A. Bowler 3, M. Bravo1, L.
Cortese1,2, C. Foster2,5, M. W. Grootes6, B. Häußler7, A. Hashemizadeh1, B. W.
Holwerda8, P. Hurley9, M. J. Jarvis3,10, C. Lidman11,12, N. Maddox13, M.
Meyer1,2, M. Paolillo14,15,16, S. Phillipps17, M. Radovich18, M. Siudek19,20,
M. Vaccari10,21, R. A. Windhorst22
1 ICRAR, The University of Western Australia, 35 Stirling Highway, Crawley, WA
6009, Australia
2 ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D)
3 Sub-department of Astrophysics, University of Oxford, Denys Wilkinson
Building, Keble Road, Oxford OX13RH, UK
4 Center for Theoretical Physics, Polish Academy of Sciences, al. Lotników
32/46, 02-668 Warsaw, Poland
5 Sydney Institute for Astronomy, School of Physics, A28, The University of
Sydney, NSW, 2006, Australia
6 Netherlands eScience Center, Science Park 140, 1098 XG Amsterdam, The
Netherlands
7 European Southern Observatory, Alonso de Cordova 3107, Vitacura, Santiago,
Chile
8 Department of Physics and Astronomy, 102 Natural Science Building,
University of Louisville, Louisville KY 40292, USA
9 Astronomy Centre, Department of Physics & Astronomy, University of Sussex,
Brighton, BN1 9QH, UK
10 Department of Physics and Astronomy, University of the Western Cape, Robert
Sobukwe Road, Bellville 7535, South Africa
11 Research School of Astronomy and Astrophysics, The Australian National
University, ACT 2601, Australia
12 Centre for Gravitational Astrophysics, College of Science, The Australian
National University, ACT 2601, Australia
13 Faculty of Physics, Ludwig-Maximilians-Universität, Scheinerstr. 1, 81679
Munich, Germany
14 INAF - Osservatorio Astronomico di Capodimonte, Salita Moiariello 16,
I-80131, Napoli, Italy
15 Dipartimento di Fisica, Universitã di Napoli ”Federico II,” via Cinthia 9,
I-80126 Napoli, Italy
16 INFN - Sezione di Napoli, via Cinthia 9, I-80126 Napoli, Italy
17 H. H. Wills Physics Laboratory, University of Bristol, Tyndall Avenue,
Bristol, BS8 1TL, UK
18 INAF - Osservatorio Astronomico di Padova, vicolo dell’Osservatorio 5,
I-35122 Padova, Italy
19 Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of
Science and Technology, 08193 Bellaterra (Barcelona), Spain
20 National Centre for Nuclear Research, ul. Pasteura 7, 02-093, Warsaw,
Poland
21 INAF - Istituto di Radioastronomia, via Gobetti 101, 40129 Bologna, Italy
22 School of Earth & Space Exploration, Arizona State University, P.O. Box
871404, Tempe, AZ 85287-1404, USA E-mail<EMAIL_ADDRESS>
(Accepted XXX. Received YYY; in original form ZZZ)

###### Abstract

The Deep Extragalactic VIsible Legacy Survey (DEVILS) is an ongoing high-
completeness, deep spectroscopic survey of $\sim$60,000 galaxies to Y$<$21.2
mag, over $\sim$6 deg2 in three well-studied deep extragalactic fields: D10
(COSMOS), D02 (XMM-LSS) and D03 (ECDFS). Numerous DEVILS projects all require
consistent, uniformly-derived and state-of-the-art photometric data with which
to measure galaxy properties. Existing photometric catalogues in these regions
either use varied photometric measurement techniques for different
facilities/wavelengths leading to inconsistencies, older imaging data and/or
rely on source detection and photometry techniques with known problems. Here
we use the ProFound image analysis package and state-of-the-art imaging
datasets (including Subaru-HSC, VST-VOICE, VISTA-VIDEO and UltraVISTA-DR4) to
derive matched-source photometry in 22 bands from the FUV to 500$\mu$m. This
photometry is found to be consistent, or better, in colour-analysis to
previous approaches using fixed-size apertures (which are specifically tuned
to derive colours), but produces superior total source photometry, essential
for the derivation of stellar masses, star-formation rates, star-formation
histories, etc. Our photometric catalogue is described in detail and, after
internal DEVILS team projects, will be publicly released for use by the
broader scientific community.

###### keywords:

methods: observational - techniques: photometric - catalogues - surveys -
galaxies: general - galaxies: evolution

††pubyear: 2020††pagerange: Deep Extragalactic VIsible Legacy Survey (DEVILS):
Consistent Multi-wavelength Photometry for the DEVILS Regions (COSMOS, XMMLSS
& ECDFS)–References

##  1 Introduction

The advent of large area, high-completeness, spectroscopic surveys of the
local Universe, coupled with deep panchromatic multi-wavelength imaging, and
resulting robustly derived galaxy and environmental properties, have been
revolutionary over the past two decades. Surveys such as the Sloan Digital Sky
Survey (SDSS, York et al., 2000; Abazajian et al., 2009) and the Galaxy and
Mass Assembly (GAMA, Driver et al., 2011; Hopkins et al., 2013; Liske et al.,
2015; Driver et al., 2016a; Baldry et al., 2018) have compiled extensive
databases of galaxy locations, distances, environments and physical properties
leading to a complete and comprehensive analysis of ‘typical’ galaxies ($i.e.$
$\gtrsim$0.1M∗, one tenth the characteristic mass in the stellar mass
function, and above) in the relatively local Universe. These surveys have
fundamentally changed our understanding of the distribution of stellar mass
($e.g.$ Baldry et al., 2012; Wright et al., 2017), star-formation ($e.g.$
Kauffmann et al., 2003; Brinchmann et al., 2004; Davies et al., 2016b, 2017,
2019b), gas and stellar phase metallicity ($e.g.$ Tremonti et al., 2004; Lara-
López et al., 2013; Bellstedt et al., 2021), large-scale structure and group
environments ($e.g.$ Tegmark et al., 2004; Yang et al., 2007; Robotham et al.,
2011; Blake et al., 2013; Alpaslan et al., 2014), pairs of galaxies ($e.g.$
Patton et al., 2013; Robotham et al., 2014) and the impact of these
environments on galaxy properties ($e.g.$ Blanton et al., 2005; Ellison et
al., 2008; Peng et al., 2010; Patton et al., 2011; Robotham et al., 2013;
Alpaslan et al., 2016; Davies et al., 2015b, 2016a, 2019a).

However, this approach of successfully combining extensive panchromatic multi-
wavelength imaging with high completeness spectroscopy to consistently measure
galaxy and environmental properties has largely been limited to the relatively
local Universe. While this can tell us a wealth of information regarding the
end-point of the galaxy evolution process to date, it tells us little about
the astrophysical process occurring over the preceding billions of years of
Universal evolution. While numerous surveys have aimed at probing the
evolution of galaxies outside of the local Universe ($z>0.3$), they can not be
well-matched to the local studies of SDSS and GAMA due to sometimes complex
selections functions, incompleteness and/or varying analysis techniques. Until
recently, the state of the art survey which probed this epoch (zCOSMOS-bright,
Lilly et al., 2007) was encumbered by its very small area ($\sim$1deg2),
sparse sampling, and complex footprint (due to slit-mask spectroscopy). This
ultimately leads to low completeness (only $\sim 50\%$ to $i<22$, see Davies
et al., 2015). Other surveys at this epoch have focused on the sparse sampling
of colour-selected populations over large volumes, such as the VIMOS Public
Extragalactic Redshift Survey (VIPERS, Guzzo et al., 2014; Scodeggio et al.,
2018) and DEEP2/3 (Cooper et al., 2012; Newman et al., 2013). The designs of
these surveys, while matched to their specific science goals, are not tuned to
provide a comparable sample to more local studies and are inadequate for
studying the evolution of galaxy groups, mergers, sub-Mpc structure and the
influence of environment on galaxy evolution - a key requirement in
understanding the evolutionary processes which are shaping galaxies.

The new Deep Extragalactic VIsible Legacy Survey (DEVILS, Davies et al., 2018)
is a high completeness, deep spectroscopic survey aimed at providing a direct
counterpart to GAMA but at a much earlier epoch ($0.3<z<1$). Unlike previous
samples DEVILS does not apply any colour selection, does not sparsely sample
the population ($i.e.$ is flux-limited only), and will use identical source
selection and analysis techniques to samples in the local Universe. This will
allow similar studies to GAMA and SDSS, but at a much earlier point in cosmic
time. Importantly, within DEVILS we will apply identical data processing and
analysis techniques to GAMA to minimise any potential errors/inconsistencies
in the approach applied, $i.e.$ it is difficult to compare measurements from
surveys at low and high redshift, which use different selection methods,
approaches and measurement techniques leading to potential errors when probing
galaxy evolution processes.

The foundation of many derived astrophysical properties of galaxies are robust
observed images/photometric measurements coupled with redshifts. With just
these data products in hand, an extensive array of measurements can be made,
such as stellar masses (Kauffmann et al., 2003; Baldry et al., 2010; Taylor et
al., 2015), star-formation rates (Brinchmann et al., 2004; Davies et al.,
2016b) and star-formation histories ($e.g.$ Kauffmann et al., 2003; Bellstedt
et al., 2020, Thorne at al 2021), metallicities ($.e.g.$ Tremonti et al.,
2004; Bellstedt et al., 2021, Thorne et al in prep.), morphological
classifications ($e.g.$ Moffett et al., 2016; Hashemizadeh et al., 2021) and
structural component analyses ($e.g.$ Lange et al., 2015). The DEVILS
spectroscopic observations are currently being undertaken at the Anglo-
Australian Telescope (AAT) (see Davies et al., 2018, and below). However, the
next stage of producing these derived properties for our sample is to acquire
robust photometric measurements of galaxies in the DEVILS regions.

Importantly for direct comparisons to $z\sim 0$, this photometry must be
derived using an almost identical method to that used in GAMA. Recently,
Bellstedt et al. (2020), produced new multi-wavelength photometric
measurements for the GAMA sample using the ProFound (Robotham et al., 2018)
source detection and photometry code. In this work we apply the same process
(albeit with small changes where appropriate) to the deep photometric data
available in the DEVILS regions. While there are existing photometric
catalogues in these fields (most notably in the COSMOS region from COSMOS2015
and G10/COSMOS, Laigle et al., 2016; Andrews et al., 2017), these catalogues
typically used imaging data that has since been superseded by more recent
higher quality data, and disparate measurement techniques at different
wavelengths, which are then matched together in catalogue space. In addition,
these samples were compiled with pre-Gaia astrometry, which can now be
improved upon using the updated astrometric accuracy Gaia provides. This can
lead to inconsistencies/errors from $e.g.$ errors in position matching and/or
blending of sources. In addition, no consistent photometry currently exists
across all of the DEVILS regions (COSMOS, XMMLSS, ECDFS). With this in mind,
in this work we derive new photometric measurements in the DEVILS regions
using the most up-to-date imaging available, in a consistent way from FUV to
FIR wavelengths and in a largely identical manner to GAMA to allow direct
comparisons to $z\sim 0$.

This photometry is already being used in a number of projects within the
DEVILS survey ($e.g.$ Thorne et al., 2020; Hashemizadeh et al., 2021; Koushan
et al., 2021, Thorne et al in prep., Hashemizadeh et al in prep.) and will
form the basis for many subsequent studies. In this paper we fully document
the source finding, photometric derivation, masking, star-galaxy separation
and catalogue compilation of the photometry used in these science projects.
Following this we also utilise our new photometry catalogues to produce and
tabulate deep multi-wavelength number counts for broader use. Once our
catalogues have been used for internal team projects, they will be made
publicly available to the wider community via the DEVILS data release 1
(Davies et al in prep.).

All magnitudes reported here are in the AB system and when necessary we assume
a cosmology with H0 = 70 km s-1 Mpc1, $\Omega_{m}$ = 0.3, and
$\Omega_{\Lambda}$= 0.7.

##  2 The Deep Extragalactic VIsible Legacy Survey

Briefly, DEVILS is an ongoing spectroscopic and multi-wavelength survey being
undertaken with the Anglo-Australian Telescope (AAT). The survey aims to build
a high completeness ($>$85%) sample of $\sim$60,000 galaxies to Y$<$21.2 mag
in three well-studied deep extragalactic fields: D10 (COSMOS), D02 (ECDFS) and
D03 (XMM-LSS). This sample will provide the first high spectroscopic
completeness flux-limited sample at intermediate redshifts, allowing for the
robust parametrisation of group and pair environments in the distant Universe.
In addition to the spectroscopic survey, the DEVILS team will also compile an
extensive database of multi-wavelength-derived properties for sources in the
DEVILS spectroscopic sample, such as spectral energy distribution (SED) fits,
star-formation histories, stellar masses, star-formation rates and
metallicities (see Thorne et al in prep.), and morphologies and structural
parameters (see Hashemizadeh et al, in prep.) - with the work presented here
forming the basis of this database. The spectroscopic component of the survey
is currently $\sim$70% complete, and is due to finish spectroscopic
observations in late 2021. The science goals of the project are varied, from
the environmental impact on galaxy evolution at intermediate redshift, to the
evolution of the halo mass function over the last $\sim$7 billion years. For
full details of the survey science goals, survey design, target selection and
spectroscopic observations see Davies et al. (2018). In this paper we describe
our new multi-wavelength photometric catalogue, which will be used in the core
DEVILS science projects.

###  2.1 The DEVILS Regions

The DEVILS regions were specifically selected to cover areas with extensive
existing and upcoming imaging campaigns to facilitate broad range of science
topics.

The D10 field represents a sub-region of the Cosmic Evolution Survey field
(COSMOS, Scoville et al., 2007), covering 1.5deg2 of the UltraVISTA (McCracken
et al., 2012) region and centred at R.A.=150.04∘, Dec=2.22∘. This region is
covered by an extensive array of imaging and spectroscopic data ranging from
x-ray to low-frequency radio continuum observations (see Table 1 for data in
the FUV-FIR reguime). The DEVILS spectroscopic target catalogue in this region
is selected from the UltraVISTA DR4 Y-band observations and includes all
extragalactic sources to Y$<$21.2 mag (see Davies et al., 2018). We do not
discuss in detail the DEVILS spectroscopic campaign further in this work.

The D02 region covers the central 3 deg2 of the XMM Large-Scale-Structure
field (XMMLSS) centred at R.A.=35.53∘, Dec=-4.70∘. This region is also covered
by extensive imaging data, which is largely consistent in facility/filter
coverage to D10. The main and important difference to the imaging data in D10,
is that the NIR imaging comes from the Vista Deep Extragalactic Survey (VIDEO,
Jarvis et al., 2013), which is consistent in facility/filters to UltraVISTA
but shallower in depth (see Table 1).

Finally, the D03 region covers the central 1.5 deg2 of the Extended Chandra
Deep Field South (ECDFS) centred at R.A.=53.15∘, Dec=-28.0∘. This region
contains similar photometric datasets to D02 in the UV and NIR-FIR, but
differs in the optical (described below and see Table 1). The DEVILS
spectroscopic target catalogues in D02 and D03 are selected from the VIDEO
Y-band and also includes all extragalactic sources to Y$<$21.2 mag (see Davies
et al., 2018).

##  3 Imaging Datasets

In this section, we briefly describe the various imaging datasets in each of
the DEVILS regions, and the surveys in which they were undertaken. The
coverage of these data in each of these fields are displayed in Figures 2, 3,
4 and a summary of all data is given in Table 1. In order to display the depth
of the various imaging datasets in comparison to the typical galaxies we will
parameterise with DEVILS, Figure 1 shows the point source depth of the D10
imaging in-comparison to the spectral energy distribution of a typical galaxy
within the DEVILS spectroscopic sample (note D02/D03 are roughly comparable in
depth, but see Table 1 for differences). In the following subsections, unless
otherwise stated, when combining data using SWARP (Bertin et al., 2002) we use
the following settings: i) we set the centre of each field to the centre of
the respective DEVILS region, ii) we set the pixel scale to that of the VISTA
imaging used in our detection image (see Section 5.2 for more details), iii)
we set the image size to cover the full DEVILS region $\pm$ a 0.05 deg buffer,
iv) we do not use a weight map or mask in the combination, v) we apply a
median combining process where frames overlap in sky position (this is only
true for the GALEX and CFHT - u data in D02 and D03, all other data does not
require combination), and vi) we do not apply a background subtraction at the
SWARP combination stage as ProFound performs its own background subtraction
when extracting photometric measurements.

Figure 1: The survey-quoted nominal 5$\sigma$ point source photometric depth
of multi-wavelength data in the D10 region displaying an example of the
typical depth of data covering the DEVILS regions (arrows joined by dashed
line). The grey curves show a M∗=1010.5 M⊙ and SFR= 1 M⊙ yr-1 galaxy at
$z$=0.2, 0.4, 0.6, 0.8 and 1.0, generated using ProSpect. The depth of imaging
in the D10 region means that a typical galaxy will be detected in $GALEX$-NUV
to $Spitzer$-IRAC out to $z\sim 1$. The imaging data extends to much greater
depth than the DEVILS spectroscopic campaign.  Figure 2: Multi-wavelength data
covering the D10 region. The D10 spectroscopic region is displayed as the red
box. Note that the HSC data are only downloaded to cover the D10 region (with
a small buffer). The raw HSC data covers a larger area. Figure 3: Multi-
wavelength data covering the D02 region. The D02 spectroscopic region is
displayed as the red box. Data from GALEX, CFHT, HSC and $Spitzer$-IRAC is
combined using SWARP to only cover the D02 region, but the original data does
extend to larger areas.  Figure 4: Multi-wavelength data covering the D03
region. The D03 spectroscopic region is displayed as the red box. Note that
data from GALEX, VST and $Spitzer$-IRAC is combined using SWARP to only cover
the D03 region. The original data does extend to larger areas. Note that
Z-band VISTA data does not cover the full region, but is the only available
deep Z-band imaging the D03 field.

###  3.1 Ultraviolet

####  3.1.1 GALEX (D10/D02/D03)

The COSMOS region was observed using the Galaxy Evolution Explorer (GALEX)
space telescope as part of the Deep Imaging Survey (Zamojski et al., 2007).
The telescope was equipped with a 0.5 m mirror, a circular field of view with
1.2 deg diameter, a 1.5 ′′ pixel-1 detector and two passbands in the far and
near-ultraviolet (FUV and NUV, respectively). Observations in the D10/COSMOS
region consisted of four pointings in each band with exposure times of 45,000
s in FUV with a point spread function (PSF) full width at half-maximum (FWHM)
of 5.4 ′′ , and 50,000 s in NUV with a PSF FWHM of 5.6 ′′ . This data reaches
a 5$\sigma$ point source limiting depth of 26.0 and 25.6 mag (AB) in the FUV
and NUV channels, respectively (see Table 1 and Figure 1). In this work we
combine the four GALEX pointings in each band into a single mosaic covering
the full D10 region using SWARP. In this process background subtraction is
turned off due to the low photon statistics in the GALEX imaging resulting in
poor background subtraction (see top left panels of Figure 2).

D02 and D03 have also been observed using the Galaxy Evolution Explorer
(GALEX) space telescope as part of various imaging programs undertaken by
different survey teams. To maximise the depth and coverage of the data
available, we identify all GALEX pointings in the MAST database that overlap
with the D02 and D03 spectroscopic regions. The images are then combined using
SWARP, with background subtraction turned off (see top left panels of Figure 3
and 4). While this process results in slightly varying depth across the image,
our resultant photometry uses a local sky background estimate to determine
photometric errors, and thus takes the varying depth into account. For
reference, the 5$\sigma$ point-source depth is measured to vary from $\sim
23.5-25.5$ mag across the region.

###  3.2 Optical

####  3.2.1 CFHT - u (D10/D02)

The $u$-band images in D10 and D02 were obtained as part of the Canada-France-
Hawaii Telescope Legacy Survey (CFHT-
LS111http://www.cfht.hawaii.edu/Science/CFHLS/). In D10, the CFHT-LS deep
survey covers the central 1 deg2 of the field, but is also supplemented by
independent CFHT observations outlined in Capak et al. (2007) to cover the
full 1.5 deg2. To maximise the depth of these data and to have coverage over
the full D10 region, we combine the CFHT-LS and Capak et al. (2007) data into
a single image using SWARP. Both data sets were obtained using Mega-Prime
camera on CFHT with a 1 deg2 field of view with a native resolution of 0.18 ′′
pixel-1. For CFHT-LS the worst case seeing was 0.9 ′′ and the 5$\sigma$ point
source limiting magnitude is 26.6 mag. For the Capak et al. (2007) data the
worst case seeing was also 0.9 ′′ and the 5$\sigma$ point source limiting
magnitude is 26.4 mag. The D02 region is covered by a combination of the CFHT-
LS deep 1 region (D1, 5$\sigma$ point source depth $\sim$26.25 mag) and wide 1
region (W1, 5$\sigma$ point source depth $\sim$25.3 mag). To maximise the
depth and coverage, we obtain all D1 and W1 data from the CFHT archive and
combine using SWARP.

####  3.2.2 Subaru-HSC - g,r,i,z (D10/D02)

For our optical, $griz$, imaging in the D10 and D02 region we use the new
second public data release (PDR2 Aihara et al., 2019) of the Subaru
Telescope’s Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP, see Aihara et
al., 2018a, b). HSC is an optical imaging camera on the 8.2m mirror Subaru
Telescope, covering a 1.5 deg diameter field of view to 0.168 ′′ pixel-1
resolution. As part of the Deep+Ultra-Deep (DUD) SSP program, the HSC team
have released 31 deg2 of DUD imaging including the extended-COSMOS snd XMMLSS
regions. Data reaches a 5$\sigma$ point source limiting magnitude of 27.3,
26.9, 26.7, 26.3, 25.3 mag in $grizy$, respectively (quoted by the HSC-SSP
team). To obtain these data, we independently download overlapping 0.25
$\times$ 0.25 deg regions in all bands from the HSC data access cutout
server222https://hsc-release.mtk.nao.ac.jp/das_cutout/pdr2/ and combined using
SWARP.

####  3.2.3 VST-VOICE - u,g,r,i (D03)

Both CFHT-LS and HSC-SSP programs do not cover the D03 region due to its
southern nature. As such, for our optical, $ugri$ imaging we use the first
data release of the VST Optical Imaging of the CDFS and ES1 Fields (VOICE,
Vaccari et al., 2016) Survey. The VOICE data covers 4 deg2 of the ECDFS region
with four pointings reaching a 5$\sigma$ point source limiting magnitude of
25.3, 26.4, 26.1, 25.2 mag in $ugri$, respectively. The survey is undertaken
on the 2.6m VLT Survey Telescope (VST) using OmegaCAM with a 0.21 ′′ /pixel
resolution. In addition, the $ugri$ imaging is also supplemented by partial
coverage of the field in the VIDEO Z-band (see below).

###  3.3 Near Infrared

####  3.3.1 UltraVISTA-DR4 (D10)

The near-IR UltraVISTA survey (McCracken et al., 2012) is a recently completed
survey on the Visible and Infrared Survey Telescope for Astronomy (VISTA)
using the VISTA Infrared Camera (VIRCAM). VIRCAM covers a 1.65 deg diameter
field of view with 0.34 ′′ pixel-1 native resolution. The UltraVISTA team
observe a single pointing centred on the COSMOS region, and are resampled to a
0.15 ′′ pixel-1 resolution. Here we use the fourth UltraVISTA public data
release (DR4333http://ultravista.org/release4/), which comprises of stacked Y,
J, H, Ks images using data taken between 2009 and 2016, with a total exposure
time of $>$11 h in each band/pixel. Typical FWHM seeing of the stacked data is
$\sim$0.75 ′′ and the 5$\sigma$ point source limiting magnitudes are 24.7,
24.5, 24.1, 24.5 mag, in the deep regions and 25.8, 25.6, 25.2, 24.9 mag, in
the ultra-deep region (see McCracken et al., 2012, for details) in Y, J , H
and Ks respectively. Data are downloaded from the ESO public archive.

####  3.3.2 VIDEO (D02/D03)

Complementary to the UltraVISTA imaging in D10, the VISTA Deep Extragalactic
Observations (VIDEO Jarvis et al., 2013) Survey has covered 12 deg2 of three
deep extragalactic fields using VISTA-VIRCAM in Z, Y, J, H and Ks bands -
completely covering the D02 and D03 regions in YJHKs. Unlike UltraVISTA, VIDEO
data are resampled to a 0.2 ′′ pixel-1 resolution. For our catalogues we use
the VIDEO team-internal 2019-11-08 stacked images, reaching full survey depth.
Data with $>0.9$ ′′ seeing are rejected from these stacks and final 5$\sigma$
point source limiting magnitudes are 25.7, 24.6, 24.5, 24.0 and 23.5 mag in Z,
Y, J, H and Ks respectively. In D02 we use only YJHKs for our imaging
catalogues and opt to use the HSC z-band data to be consistent with D10.
However, as noted above, in D03 we do not have HSC coverage and therefore use
the VIDEO Z-band data, which due to an updated strategy implemented during the
VIDEO survey is incomplete and shallower (see Figure 4). For details of the
VIDEO survey and data, see Jarvis et al. (2013).

###  3.4 Mid Infrared

####  3.4.1 S-COSMOS and Spitzer-SPLASH (D10)

The COSMOS $Spitzer$ survey (S-COSMOS, Sanders et al., 2007), is a 3-160
$\mu$m survey of the COSMOS region using all seven bands of the $Spitzer$
Infrared Array Camera (IRAC) and the Multiband Imaging Photometer for Spitzer
(MIPS) instruments. The IRAC instrument covers four NIR-MIR filters centred at
3.6, 4.5, 5.6, 8.0 $\mu$m over a 27 arcmin2 field of view with native 1.2 ′′
pixel-1 resolution. MIPS covers three MIR-FIR filters centred at 24, 70 and
160 $\mu$m with native resolution of 1.2, 4.0 and 8.0 ′′ pixel1 respectively.

Following the S-COSMOS campaign, and during $Spitzer^{\prime}s$ ‘warm’ mission
phase once the cryogens had been exhausted, the COSMOS field was observed by
the Spitzer Large Area Survey with Hyper-Suprime-Cam (SPLASH) survey at 3.6
and 4.5 $\mu$m increasing the depth and area coverage in these bands (see Lin
et al., 2016; Laigle et al., 2016). Both SPLASH and S-COSMOS IRAC data have
been resampled to 0.6 ′′ pixel-1 resolution by their respective teams, with
typical PSF FWHM of 1.7, 1.7, 1.9 and 2.0 ′′ for the IRAC bands respectively.

Here we use the superior SPLASH IRAC 3.6 and 4.5 $\mu$m data reaching a
5$\sigma$ point source limiting magnitude of 24.9 in both bands, and S-COSMOS
IRAC 5.6 and 8.0 $\mu$m, and MIPS 24 and 70 $\mu$m reaching a 5$\sigma$ point
source limiting magnitude of 22.4, 22.3, 19.3, and 14.2 mag respectively. We
do not use the MIPS 160 $\mu$m data in favour of the deeper $Herschel$ data
that exist in this region (see below).

####  3.4.2 SERVS and SWIRE (D02/D03)

Both D02 and D03 are covered by the $Spitzer$ Extragalactic Representative
Volume Survey (SERVS, Mauduit et al., 2012) survey at 3.6 and 4.5 $\mu$m
(IRAC1 and IRAC2). SERVS is an 18 deg2 medium-deep survey with the post-
cryogenic Spitzer Space Telescope reaching $\sim$2 mJy (AB=23.1 mag) depth of
five deep astronomical fields (ELAIS-N1, ELAIS-S1, Lockman, ECDFS and XMMLSS).
We use the DR1 release of the SERVS data products which reach a 5$\sigma$
point source limiting magnitudes of 22.48 and 22.07 in S36 and S45,
respectively.

At longer wavelengths, both fields are covered by the $Spitzer$ Wide-area
InfraRed Extragalactic (SWIRE, Lonsdale et al., 2003) survey at 3.6, 4.5, 5.8,
8.0, 24,70 and 160 $\mu$m (IRAC1234, MIPS123). SWIRE has surveyed $\sim$50
deg2 centred on the same deep fields as SERVS, but to a shallower depth. Here
we use the DR5 SWIRE images at 5.8, 8.0, 24 and 70 $\mu$m, opting to use the
deeper SERVS data for IRAC 1 and 2 and $Herschel$ data for 160 $\mu$m. The DR5
data reaches 5$\sigma$ point source limiting magnitudes of 19.7, 19.96, 18.0
at 13.26 mag at 5.8, 8.0, 24 and 70 $\mu$m, respectively. Both SERVS and SWIRE
data are obtained from the NASA/IPAC Infrared science
archive444https://irsa.ipac.caltech.edu/frontpage/.

###  3.5 Far Infrared

####  3.5.1 $Herschel$-PACS Evolutionary Probe (D10)

The PACS (Photodetector Array Camera and Spectrometer) Evolutionary Probe
(PEP, Lutz et al., 2011) was a $Herschel$ key program covering a number of
deep fields at 100 and 160 $\mu$m, including COSMOS. The PACS instrument has a
field of view of 1.75 $\times$ 3.5 arcmin, and 1.2 and 2.4 ′′ pixel-1
resolution at 100 and 160 $\mu$m, respectively.

In this paper we use the first PEP data
release555http://www.mpe.mpg.de/ir/Research/PEP/DR1 which imaged the COSMOS
region reaching a 5$\sigma$ point source limiting magnitude of 14.1 and 13.3
mag at 100 and 160 $\mu$m, with a PSF FWHM of 7.4 and 11.3 ′′ , respectively.

####  3.5.2 $Herschel$-HerMES (D10/D02/D03

The $Herschel$ Multi-Tiered Extragalactic Survey (HerMES, Oliver et al., 2012)
is a far-IR survey conducted using $Herschel^{\prime}s$ Spectral and
Photometric Imaging Receiver (SPIRE). As part of HerMES, the COSMOS region was
observed at 250, 350 and 500 $\mu$m with a pixel scale 6.0, 8.3 and 12.0 ′′
pixel-1 and FWHM of 18.15, 25.15 and 36.3 ′′ , respectively. The XMMLSS and
ECDFS regions were also observed by HerMES and cover both PACS 100 and 160
$\mu$m and SPIRE 250, 350 and 500 $\mu$m.

In this work we use the second HerMES data
release666http://hedam.lam.fr/HerMES/ which reaches 5$\sigma$ point source
limiting magnitudes of 12.4, 11.6, 14.1, 14.4 mag and 13.9 at 100, 160, 250,
350 and 500 $\mu$m, respectively. Construction of the HerMES images is
described in detail in Levenson et al. (2010) and Viero et al. (2013).

Note, for the rest of this paper we will predominantly only show figures and
quote numbers for the D10 region. The reason for this is two-fold, firstly the
D10 region has superior existing photometric catalogues with which to compare
to, and secondly, for ease of description. However, we note that our process
is identical in the D02/D03 regions.

Table 1: Overview of multi-wavelength data in the DEVILS regions used in this
photometric analysis. For further details see sections 3. Nominal Depth is the
depth quoted by the respective survey, while Measured Depth is the depth to
galaxy sources derived from the turn-over point in the galaxy number counts
(see Section 8) For the common data in D02 and D03 the Measured Depth column
displays D02 ”/” D03 depths.

Field | Common | Facility | Survey | Band | Central | Nominal | Measured | Zero- | Ref
---|---|---|---|---|---|---|---|---|---
| Name(s) |  |  |  | Wavelength | Depth | Depth | point |
|  |  |  |  | ($\mu$m) | (5$\sigma$ AB) | (AB) | (AB) |
D10 | COSMOS | GALEX | GALEX-DIS | FUV | 0.154 | 26.0 | 28.42 | 18.82 | Zamojski et al. (2007)
|  |  |  | NUV | 0.231 | 25.6 | 27.37 | 20.08 |
|  | CFHT | CFHT-COSMOS | u | 0.379 |  $>$26.4 | 26.36 | 30.00 | Capak et al. (2007)
|  | Subaru | HSC-SSP (DUD) | g | 0.474 | 27.3 | 26.09 | 27.80 | Aihara et al. (2019)
|  |  |  | r | 0.622 | 26.9 | 25.82 | 27.70 |
|  |  |  | i | 0.776 | 26.7 | 25.09 | 27.60 |
|  |  |  | z | 0.893 | 26.3 | 25.41 | 27.80 |
|  |  |  | y | 1.00 | 25.3 | 24.90 | 26.20 |
|  | VISTA | UltraVISTA | Y | 1.02 |  $>$24.7 | 25.46 | 30.00 | McCracken et al. (2012)
|  |  |  | J | 1.26 |  $>$24.5 | 25.22 | 30.00 |
|  |  |  | H | 1.65 |  $>$24.1 | 24.98 | 30.00 |
|  |  |  | Ks | 2.16 |  $>$24.5 | 24.75 | 30.00 |
|  | Spitzer | SPLASH | S36 | 3.53 | 24.9 | 24.66 | 21.58 | Laigle et al. (2016)
|  |  |  | S45 | 4.47 | 24.9 | 24.77 | 21.58 |
|  |  | S-COSMOS | S58 | 5.68 | 22.4 | 23.86 | 21.58 | Sanders et al. (2007)
|  |  |  | S80 | 7.75 | 22.3 | 23.90 | 21.58 |
|  |  |  | MIPS24 | 23.5 | 19.3 | 18.53 | 20.15 |
|  |  |  | MIPS70 | 70.4 | 14.2 | - | 17.53 |
|  | $Herschel$ | PEP | P100 | 98.9 | 14.1 | 14.93 | 8.90 | Lutz et al. (2011)
|  |  | PEP | P160 | 156 | 13.3 | 14.07 | 8.90 |
|  |  | HerMES | S250 | 250 | 14.1 | 14.24 | 11.44† | Oliver et al. (2012)
|  |  | HerMES | S350 | 350 | 14.4 | 14.30 | 11.43† |
|  |  | HerMES | S500 | 504 | 13.9 | 14.64 | 11.44† |
D02 | XMMLSS | GALEX | multiple | FUV | 0.154 |  $\sim$25.0 | 27.32/27.04 | 18.82 | multiple
& D03 | & ECDFS |  |  | NUV | 0.231 |  $\sim$25.0 | 26.70/27.02 | 20.08 |
|  | VISTA | VIDEO | Y | 1.02 | 24.6 | 24.65/24.82 | 30.00 | Jarvis et al. (2013)
|  |  |  | J | 1.26 | 24.5 | 24.34/24.45 | 30.00 |
|  |  |  | H | 1.65 | 24.0 | 24.05/24.14 | 30.00 |
|  |  |  | Ks | 2.16 | 23.5 | 23.84/23.9 | 30.00 |
|  | Spitzer | SERVS | S36 | 3.53 | 22.48 | 23.45/23.97 | 21.58 | Mauduit et al. (2012)
|  |  |  | S45 | 4.47 | 22.07 | 23.08/23.98 | 21.58 |
|  |  | SWIRE | S58 | 5.68 | 19.70 | 22.31/22.64 | 21.58 | Lonsdale et al. (2003)
|  |  |  | S80 | 7.75 | 19.96 | 22.33/22.70 | 21.58 |
|  |  |  | MIPS24 | 23.5 | 18.00 | 18.26/18.21 | 20.15 |
|  |  |  | MIPS70 | 70.4 | 13.26 | -/- | 17.53 |
|  | $Herschel$ | HerMES | P100 | 98.9 | 12.4 | 13.77/13.67 | 8.90 | Oliver et al. (2012)
|  |  | HerMES | P160 | 156 | 11.6 | 13.07/12.98 | 8.90 |
|  |  | HerMES | S250 | 250 | 14.1 | 13.99/13.99 | 11.44† |
|  |  | HerMES | S350 | 350 | 14.4 | 14.03/14.07 | 11.43† |
|  |  | HerMES | S500 | 504 | 13.9 | 14.27/14.29 | 11.44† |
D02 | XMMLSS | CFHT | CFHT-LS | u | 0.379 |  $>$25.3 | 25.68 | 30.00 | CFHTLS
|  | Subaru | HSC-SSP (DUD) | g | 0.474 | 27.3 | 25.50 | 27.80 | Aihara et al. (2019)
|  |  |  | r | 0.622 | 26.9 | 25.19 | 27.70 |
|  |  |  | i | 0.776 | 26.7 | 24.94 | 27.60 |
|  |  |  | z | 0.893 | 26.3 | 24.75 | 27.80 |
|  |  |  | y | 1.00 | 25.3 | 23.97 | 26.20 |
D03 | ECDFS | VST | VOICE | u | 0.379 | 25.3 | 25.76 | 30.00 | Vaccari et al. (2016)
|  |  |  | g | 0.474 | 26.4 | 25.58 | 30.00 |
|  |  |  | r | 0.622 | 26.1 | 25.33 | 30.00 |
|  |  |  | i | 0.776 | 25.2 | 24.89 | 30.00 |
|  | VISTA | VIDEO | Z | 0.893 | 25.7 | 25.65 | 30.00 | Jarvis et al. (2013)

† SPIRE maps are in units of Jansky per Beam and to generate these zero-points
we have added a factor $2.5\log_{10}(B/N^{2})$ where B is the beam size given
as 373, 717, and 1494 sq ′′ and N is the pixel size given as 6.0, 8.3 and 12
′′ in 250, 350 and 500$\mu$m, respectively.

##  4 The need for a new COSMOS (D10) photometric catalogue

There have been a number of multi-wavelength photometric catalogues produced
in the COSMOS region over the past two decades, such as those outlined in
Capak et al. (2007), Laigle et al. (2016) and for the central 1 deg2 discussed
in Andrews et al. (2017) and Driver et al. (2018). However, as noted in
Section 1, these catalogues have relied on software that causes some known
issues when deriving total photometry catalogues across a broad range of
source types and sizes. They have relied on table-matching of photometry
derived by different teams using varied techniques, and/or do not contain the
most up-to-date imaging in the region (such as UltraVISTA DR4 and HSC). As
such, we now derive a new photometry catalogue which uses a state-of-the-art
photometry code, designed to overcome issues in current catalogues. This
enables us to measure photometry consistently from the UV-FIR with a single
package on homogenised data, and include the most up-to-date imaging campaigns
in this region.

Through the process of producing the G10/COSMOS catalogue outlined in Andrews
et al. (2017) and, using a similar method, the GAMA photometric catalogues
(see Driver et al., 2016a; Wright et al., 2016), we identified a number of
issues when determining total source photometry. Briefly, within both of these
surveys initial source detection was undertaken using Source Extractor (Bertin
& Arnouts, 1996) to define elliptical apertures. However, it was quickly
identified that these apertures are prone to some issues, such as bright
galaxy fragmentation into a number of sub-apertures, and highly erroneous
apertures, largely due to the aperture following an isophotal bridge to loop
around a nearby bright star (these issues mostly arise because of the
difficulty in simultaneously measuring fluxes for both very bright and very
faint sources). For details of these issues see Robotham et al. (2018) and
Davies et al. (2018). These errant apertures led to a significant fraction
($\sim$10%) of sources having poorly measured photometry. To overcome these
issues in the G10/COSMOS sample and in GAMA (see Andrews et al., 2017; Wright
et al., 2016, respectively), we undertook a systematic, and time-consuming,
visual inspection and manual fixing of the errant apertures both within the
teams and via a citizen science project (AstroQuest
777https://astroquest.net.au/).

Motivated by the level of manual fixing required in the GAMA and G10/COSMOS
data, we developed a new source finding and photometry code, ProFound
(Robotham et al., 2018). Briefly, the ProFound code has three fundamental
differences to its approach over Source Extractor. Firstly, ProFound does not
measure source photometry based on elliptical apertures, but identifies and
retains the source isophote, allowing for both simple and irregular shapes
(called segments). Secondly, these initial segments are dilated until the flux
converges providing a pseudo-total flux. Thirdly, ProFound applies a watershed
deblending approach where during the dilation, segments cannot overlap
($i.e.$, all the flux in any one pixel is allocated to one galaxy only). This
is different to Source Extractor, where hierarchical or nested-deblending
allows for overlapping elliptical apertures and the possibility of flux being
double counted. While the merits and failings of these approaches can be
argued - through numerous tests we have found that the ProFound approach is
far less likely to go catastrophically wrong and produces more robust
photometric measurements. For full details on ProFound and numerous tests, we
refer the reader to the code description paper Robotham et al. (2018) and four
recent applications to the DEVILS input catalogue (Davies et al., 2018), GAMA
photometry (Bellstedt et al., 2020), the VISTA VIKING datasets (Koushan et
al., 2021) and evolution of the high redshift stellar mass function (Adams et
al., 2020).

In this work we apply the ProFound software to the state-of-the-art UV-FIR
photometric data in the DEVILS regions. This is done in two modes, firstly
using the vanilla ProFound multi-band approach to the UV-MIR data (described
in Section 5), and secondly applying a new Bayesian technique to extract
photometry in the unresolved MIR-FIR regime (described in Section 6). A
similar approach has been applied in our shallower updated GAMA photometry, as
outlined in Bellstedt et al. (2020). We note that we do not present new
photometric measurements to the medium- and narrow-band Subaru Suprime-cam
data in the D10 (COSMOS) region. This is largely due to the fact that we are
aiming for consistent photometric catalogues across all of the DEVILS fields.
As comparable data does not exist in the D02 and D03 regions, we do not
include it here. We also note that in our SED fitting analysis of the D10
sample, presented in Thorne et al. (2020), we find little improvement/changes
to measurements of stellar mass and star-formation rate when including the
medium- and narrow-band data. Where this data is of significant benefit is in
the derivation of photometric redshifts where finer wavelength sampling allows
for greater redshift precision. However, we do not derive new photometric
redshifts here as the COSMOS2015 sample already obtains robust photometric
redshift measurements for these systems using the medium-band and narrow-band,
and it is unlikely that we could significantly improve upon this.

Figure 5: Example of a fragmented galaxy that has been rebuilt. Image shows
the ProFound segmentation map for a galaxy that has been split into multiple
segments. Segments are bounded by colours assigned by the segments flux from
blue (faint) to red (bright). The top panel shows the galaxy before regrouping
and the lower panel shows the segments after regrouping. Figure 6: Example of
a complex region that required manual intervention to fix segments. The top
panel shows the region before manual fixing and the lower panel shows the
final segments after manual fixing. Segments which have been manually added,
after some regrouping, are bordered by dark blue and red lines and largely
fall in the centre of the complex.

##  5 UV-MIR Photometric Measurements

###  5.1 ProFound Source Detection, Rebuilding Fragmented Galaxies and
Splitting Merged Systems

To use ProFound to first identify target sources, we produce an inverse
variance weighted stacked image from the UltraVISTA (D10) and VIDEO (D02/D03)
Y, J, and H band images. Note that we do not use Ks because the UltraVISTA Ks
image does not cover the same spatial extent as the other bands in D10 and has
a small region of the DEVILS field missing. In D02/D03 we use the VIDEO Y, J,
and H images only for consistency. This stacking allows us to maximise the
depth in the initial detection image and remove any artefacts associated with
a single band. This is a similar process to that outlined in Davies et al.
(2018) for the DEVILS input catalogues. This stacking process is handled
internally by ProFound using the profound-MultiBand command, allowing the user
to specify one or more bands for source detection. If more than one band is
supplied the images are inverse variance weighted stacked. We note here that
one could opt to add additional bands in our stacking procedure ($i.e.$ at
bluer wavelengths) to both increase the source identification depth and
potentially include bluer sources which are undetected in the NIR. However, we
do not do this for a number of reasons: i) We wish to have a DEVILS survey
selection band which is close to a single band measurement of stellar mass
($i.e.>$4000Å in the rest-frame) for all galaxies to $z\sim 1$. This
necessitates the NIR bands. We use our stack for source detection and then
Y-band magnitude to select our spectroscopic sample. As such, we would not
target faint blue galaxies that are undetected in the Y-band. ii) The NIR data
used here is already far deeper than that which we would use for science. So
the faint blue sources that are undetected in the NIR are unlikely to be
included in our science samples. This is especially true when performing a
stellar mass-based selection ($i.e.$ as in Thorne et al., 2020), and which
will be done for all DEVILS core science, and iii) we wish to have consistency
in the detection filters across all DEVILS fields. As noted previously, Z-band
data does not exist over all of the D03 region and the I-band data comes from
different facilities (HSC and VST) in different fields.

Once the stack is produced, ProFound then identifies all sources of flux in
this stacked image producing a segmentation map designed to separate flux from
individual sources (see Robotham et al., 2018, for further details). ProFound
produces two different segmentation maps, first a colour-optimised
segmentation that covers the central high-signal-to-noise region of sources
prior to the ProFound segment dilation process, and a total-optimised
segmentation that dilates the colour-optimised segments in an attempt to
extract total flux measurements associated with a source. The colour-optimised
segments are essentially tight detection segments (akin to a small fixed-size
aperture - but tuned to the shape of the source’s central distribution of
light), which do not encompass all of galaxy flux. This will best-represent
the overall SED shape, but not normalisation. This means that the colour-
optimised segments are best used for scientific analyses that do not require
total flux measurements ($i.e.$ photometric redshifts), while total-optimised
segments are best used for the converse ($i.e.$ stellar mass and SFR
measurements, etc). Both of these segmentation maps are used in our analysis.
For running our ProFound source detection we use the Y, J, H band images
trimmed to the DEVILS spectroscopic region, but with a 0.1 deg2 buffer to
minimise any issues with sources that overlap the edge of the field. Finally,
due to memory issues, ProFound can not be run over a full DEVILS field in a
single process. To overcome this, we split the field in to a number of
overlapping subregions and undertake our photometric detection/measurements in
each region independently (8 regions in D02 and also 4 regions in D10 and
D03). These are later combined in a process described in Section 5.2. For
example, in total ProFound identifies $\sim 951,000$ unique segments across
our four sub-regions in D10.

While the ProFound segmentation map successfully identifies individual sources
and robustly maps their extent, a known problem with most automated source
finding algorithms (including ProFound — but to a lesser degree than $e.g.$
SExtractor) is the fragmentation of bright galaxies. This problem has been
explored in the derivation of new photometry in GAMA (Bellstedt et al., 2020)
and a number of enhancements to ProFound were made to assist with fixing this
issue. The first two enhancements were the introduction of the ProFound-
reltol and cliptol parameters in the source finding mode. Reltol allows
ProFound to keep very extended and flocculent spiral galaxies intact, while
having little negative impact on the fainter source deblending that parameters
will tend to be optimised for (since this is where almost all of our survey
sources exist). Cliptol specifies the saddle point flux above which segments
are always merged, regardless of competing criteria. This is useful for very
bright objects with complex image artefacts ($e.g.$ in the regions around
bright stars), and allows for proper reconstruction, which might otherwise be
hugely fragmented into many small segments. We do however utilise a bright
star mask (see Section 5.4), which means that the resulting segments around
bright stars are somewhat cosmetic. These new parameters are described in more
detail in Bellstedt et al. (2020).

Despite the inclusion of these new ProFound parameters, some low-level
fragmentation of bright galaxies still occurs. To fix this, and regroup
segments belonging to the same bright object, an in-house interactive tool was
developed that allows users to view a thumbnail of an object and click on
segments to be regrouped. This tool is available through the profoundSegimFix
function within ProFound, and is applied to our total-photometry segments.

As part of its output, ProFound provides information on $group$ statistics,
where a group is defined as touching segments in a $cluster$ of segments
around a source, or multiple sources (see Robotham et al., 2018). These
$group$ statistics allow us to easily identify potentially fragmented galaxies
in our segmentation maps. Rather than visually inspecting every group, we
determine the groups that need to be inspected based on the following
criteria:

i) The group of touching segments must contain at least three segments (this
mean that there might be cases where a single galaxy has been split in two and
it would not be selected as an object to fix, but because the field is so deep
and there are lots of segments there are very few instances where this is the
case).

ii) We only inspect groups with a total group magnitude (across the three
stacked detection bands) between 12 and 20.5 mag to reduce the number of
groups that needed to be inspected.

iii) We also only inspect groups where the brightest pixel is at least 500
pixels away from the edge of the frame (this removes groups in our sub-region
overlap areas).

iv) Finally, we also exclude masked objects such as stars and artefacts from
the visual re-grouping process, because the majority of touching segments
reside in the ghosting surrounding stars.

For example, in the D10 region this resulted in 12,354 groups to visually
inspect. Of these, 1,272 required some manual intervention (10.3 per cent). An
example of an object whose segments have been regrouped in this way can be
seen in Figure 5, where the bottom panel shows the resulting segmentation map
after merging. For each of the four input sub-regions, one output file was
produced that recorded which segments (as determined by the detection phase of
ProFound) needed to be regrouped.

In addition to the segment regrouping, upon visual inspection of the ProFound
outputs, we found that a small number of large, highly clustered sources were
merged into a single ProFound segment - and thus required $ungrouping$. The
profoundSegimFix function also allows for segments to be manually created
using a GUI tool where the new segments are drawn onto an image. In order to
fix all erroneously grouped regions, we first ignore stars, artefacts and
masked regions (see below) and then select the superset of segments which are
either: i) in the top 100 Y-band brightest segments in their subregion, ii) in
the top 100 Y-band largest segments in their subregion, and iii) objects where
the brightest Y-band pixel within a segment in $>4$ ′′ away from the flux-
weighted central pixel of the segment (both provided by ProFound). This
process visually selected all erroneously grouped regions in our subregions
and lead to $\sim$30-60 regions to visually inspect per subregion ($i.e.$
there is significant overlap in the selections). Figure 6 shows one of the
extremely complex regions in the D10 field that requires both segment
regrouping and ungrouping. The top panel show the ProFound segmentation map
prior to manual fixing, while the bottom panel shows the region following
manual fixing. Segments that have been manually included in the ungrouping
process are shown bounded by blue/red lines. We note here, that while this
process requires manual intervention, it is far less time-consuming that the
manual fixing process discussed in Andrews et al. (2017) and Wright et al.
(2016).

Once complex regions have been both manually regrouped and ungrouped, where
appropriate, we generate a new segmentation map for each subregion and use
this for the remainder of the photometry pipeline.

Figure 7: Galactic Extinction in the DEVILS regions based on the Planck E(B-V)
maps. Top left = D10, Top right = D03, Bottom = D02. Note that we use a
different colour bar scaling for each field as the extinction is low, but
significantly different in each region. Thus if the same scale is used, all
detail would be removed. The dashed vertical line in D02 displays the
separation between the D02A (left) and D02B (right) regions.

###  5.2 Multiband Photometry with ProFound and Sub-Region Combination

After the initial segmentation map has been defined and manual fixing applied
in each of our subregions, these segments are then used to measure both total-
and colour-optimised photometry in the UV-MIR bands (GALEX-FUV to
$Spitzer$-IRAC 8.0 $\mu$m). Our initial process is only applied to the UV-MIR,
where the pixel-scale/seeing are comparable and source blending/confusion
between bands is low. The process for measuring our photometry at longer
wavelengths using PSF fitting is described in Section 6. Note that in the IRAC
bands it is somewhat ambiguous as to which method would be preferred, ProFound
segmentation versus PSF extraction. As such, we initially perform both
approaches for our IRAC photometry and compare to existing MIR measurements
for these galaxies. We find that for all of the IRAC bands our ProFound
segmentation approach provides closer consistency between our new photometry
and existing catalogues. Hence, we opt to switch between methods at the
IRAC24/MIPS24 boundary. As is shown later, this choice produces tighter MIR
colours that the existing photometry catalogues.

Prior to photometric measurements, all imaging bands must first be transformed
to the same pixel scale and field size. To do this we use the SWARP package,
matching all bands to the same pixel scale and area as the stacked Y, J, and H
band image (0.15 ′′ pixel-1 for UltraVISTA in D10 and 0.2 ′′ pixel-1 for VIDEO
in D02/D03). ProFound then initially applies the colour-segmentation maps
(central high signal-to-noise region) to each band. As the PSF of our imaging
varies significantly from band to band, we can not use fixed segments for each
filter (as we would miss flux in the lower resolution data). As such, ProFound
takes the colour-optimised segmentation map as a starting, source detection,
point and then allows the segments to further dilate in an attempt to
encompass more flux until convergence is reached (see Robotham et al., 2018,
for details). This process accounts for the varying PSF, without having to
apply detailed modelling of the PSF shape in each band. ProFound then outputs
both colour and total flux measurements, errors and segment statistics (such a
radii, number of pixels, sky background, etc) in each band.

Following measurement in each band, catalogues from our sub-regions are
combined to produce a photometry catalogue for the full DEVILS field. To do
this, we combine all sub-regions to a single catalogue, and remove duplicates
in the overlapping regions, retaining the source which is closest to its sub-
region field centre. We then trim the catalogue to the extent of the DEVILS
spectroscopic field. As an example, this results in $\sim 777,000$ unique
segments in the D10 region.

###  5.3 Extinction Corrections

To correct our measured magnitudes for Galactic extinction we use the Planck
$E(B-V)$ map888HFI CompMap ThermalDustModel 2048 R1.20.fits,
https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-
maps/previews/HFI_CompMap_ThermalDustModel_2048_R1.20/index.html, converting
HEALpix values to RA and Dec positions across the field. Figure 7 displays the
Planck $E(B-V)$ values across the DEVILS regions. For each ProFound source we
then identify the closest Planck $E(B-V)$ pixel assigning the corresponding
extinction value. We then correct all magnitudes, surface brightnesses and
fluxes based on this extinction value. We determine the attenuation correction
for each band in the traditional manner ($i.e.$ $A_{x}=[A_{x}/E(B-V)]\times
E(B-V))$ using the extinction coefficients listed in Table 2 (which implicitly
use the Galactic extinction law from Schlafly & Finkbeiner, 2011). Note that
we do not apply any corrections long-ward of the IRAC bands, where emission
then arises from dust itself, and would be subtracted as part of the
background.

Table 2: Attenuation values used in conjunction with Planck $E(B-V)$ map, consistent with the local GAMA analysis.  Filter | $[A_{x}/E(B-V)]$
---|---
FUV | 8.24152
NUV | 8.20733
u | 4.81139
g | 3.66469
r | 2.65460
u | 2.07472
Z | 1.55222
Y | 1.21291
J | 0.87624
H | 0.56580
Ks | 0.36888
S36 | 0.20124
S45 | 0.13977
S58 | 0.10094
S80 | 0.09818

Figure 8: Example of the masking procedure overlaid on a H (VISTA), Y (VISTA),
r (HSC) RGB image (note that only the H and Y bands are used in our source
detection stacked image, so blue extended emission does not contribute to our
source detection). Left: ProFound segments are either masked or identified as
artefacts using three different methods. Firstly a GAIA star mask is used to
define an exclusion radius around bright stars (pink solid circle). This
radius is scaled to the GAIA g-band magnitude (see Section 5.4). All segments
within this radius are flagged as being masked (blue-segments). Secondly, we
define a region of 2.2 $\times$ the GAIA radius (pink dashed circle). Segments
within this region that are contained within the same ProFound group as the
central star and are fainter than the DEVILS spectroscopic limit (Y$>$21.2)
are also flagged as masked (red-segments). Finally, we also identify
additional artefacts using the procedure outlined in Section 5.5 \- which are
coloured orange. Right: The results of masking process. Here all masked
segments are coloured black. We highlight all Y$<$24.0 mag segments in
magenta, and Y$<$21.2 mag (the DEVILS spectroscopic limit) segments in blue,
showing that the masking process only retains segments where robust photometry
can be derived.

###  5.4 Masking

As can be seen in the top left panel of Figures 8 (and Figures 2, 3 and 4),
our input images have significant issues with ghosting around bright stars.
This ghosting is dependent on both position within the focal plane and the
brightness of individual stars. In Davies et al. (2018) we developed a method
for masking bright stars for the DEVILS input catalogue. However, in this work
we have improved our masking procedure to better exclude erroneous sources
near bright stars and minimise the area lost to our mask. This process is
carried out in a number of stages, which are visually described in Figure 8.

Initially, following the method outlined in Bellstedt et al. (2020) we select
all GAIA DR2 stars with GAIA g-band magnitude brighter than 15.75 mag in the
DEVILS regions, and define a star exclusion radius which is scaled to the GAIA
g-band magnitude, with:

$r_{star}[\mathrm{\prime}]=10^{(1.795-0.158g_{\mathrm{GAIA}})}\,\,\mathrm{and}\,[r<1.85^{\prime},g_{\mathrm{GAIA}}<15.75\,mag]$
(1)

However, unlike Bellstedt et al. (2020) who use a large conservative radius,
our radius is tuned to $just$ encompass the ghosting for all stars in our
field, excluding the minimum region possible (solid pink circles in Figure 8).
All ProFound segments within this minimum radius are classified as masked
(blue segments in Figure 8). While this masks out the majority of segments in
the ghosted region, it does miss some outlying faint segments caused by the
ghost. Following this, we then match the GAIA DR2 source positions to our
ProFound segmentation maps, finding the closest segment centre to the GAIA
position (red dots in Figure 8) and defining this as the central stellar
segment. As noted previously, ProFound fragments very bright sources, and this
also occurs for the bright stars. As such, this central segment does not
encompass all of the star’s flux (or the ghost). We therefore select all
segments which are within a $group$ (see Section 5.1) with the central GAIA-
matched segment and also within $2.2\times r_{\mathrm{star}}$ (dashed pink
circles in Figure 8). Segments that match this criteria but are fainter than
the DEVILS spectroscopic limit are also masked (red segments in Figure 8).
Finally, we class all segments defined as artefacts in the process outlined in
Section 5.5 as being masked. The right panels of Figure 8 displays two example
regions with all masked segments coloured black, showing that our process
effectively removes all erroneous sources of flux in segments across the
region. While some remnant of the stellar halo can still be seen, this is not
associated with any sources. We then define our masked region as the area
contained in masked segment areas. This process results in $\sim$1-3% of each
field being masked, with our final unmasked areas given in Table 3. For
comparison Bellstedt et al. (2020) report 5.4% of the GAMA area is masked
using their process. Note that the masked percentage within D10 is larger as
the deep UltraVISTA data has larger haloes around bright stars (see Y-band
panels in Figures 2, 3 and 4). We retain all ProFound-detected segments in our
final catalogues, but assign a mask=1 flag to sources that have been masked.

Table 3: Final unmasked areas in each of our DEVILS regions in the Y-band Field | Total | Unmasked | Percentage
---|---|---|---
| (deg2) | (deg2) | masked
D02 | 3.04 | 3.00 | 1.3%
D03 | 1.50 | 1.49 | 0.7%
D10 | 1.52 | 1.47 | 3.4%

###  5.5 Object Classification

####  5.5.1 Star, galaxy and ambiguous classification

In order to separate stars and galaxies, we perform a multi-stage process
using the ProFound-derived outputs. This process is detailed in Figure 9 \-
showing just sources for the D10 region. First we consider two separate
parameter spaces, Y-band R50 (the radius containing 50% of the source’s Y-band
flux in arcsec) vs. Ymag, and (H-Ks)-(Y-J) vs. Ymag, displayed in the left and
right columns of Figure 9, respectively. In the top panel of Figure 9 we
colour-code each of our sources by the SED-derived source type, as estimated
from the COSMOS2015 catalogue via the photometric redshift fitting code Le
Phare (where available), with the middle panels displaying the gridded galaxy
fraction. Using these we define an ambiguous region where galaxies and stars
are largely indistinguishable in each parameter space (bounded by the dashed
and dotted lines) - $i.e.$ there are roughly equal numbers of galaxies and
stars. Sources that are above the dashed line in either parameter space are
classed as galaxies, and sources below the dotted line in either parameter
space are classed as stars. Sources that fall in between these two
classification in both planes are flagged as ‘ambiguous’, and sources that are
classified as a star in one plane and a galaxy in the other are also
classified as ‘ambiguous’. The equations that define the lines are:

Galaxies (dashed lines):

$\displaystyle R_{50}>-28.49+9.52Y-1.03Y^{2}+0.05Y^{3}$
$\displaystyle-0.0011Y^{4}+8.4e^{-6}Y^{5},$
$\displaystyle(H-Ks)-(Y-J)>-0.15\,\,\,\,\,\,\mathrm{if\,\,\,Y<22.2},$
$\displaystyle(H-Ks)-(Y-J)>-0.15-0.03(Y-22.2)^{5}\,\,\,\,\,\,\mathrm{if\,\,\,Y>22.2}$

Stars (dotted lines):

$\displaystyle R_{50}<-161.33+44.89Y-4.75Y^{2}+0.24Y^{3}$
$\displaystyle-0.0061Y^{4}+5.9e^{-5}Y^{5},$
$\displaystyle-0.7<(H-Ks)-(Y-J)<-0.15\,\,\&\,\,Y<21.75$
$\displaystyle\mathrm{OR}\,\,Y<16$

Figure 9: Star-galaxy separation using measured half-light radius versus
Y-band magnitude (left) and (H-Ks)-(Y-J) colour versus Y-band magnitude
(right) in the D10 region. The top panels display the distribution of sources
in our photometric catalogue colour coded by the source type assigned via the
photometric redshift fitting code Le Phare in the COSMOS2015 catalogue. The
middle panels show the same data, but binned and colour-coded by the faction
of galaxies in each bin. We use these figures to define selection boundaries
between galaxy/ambiguous (dashed lines) and stars/ambiguous (dotted line). We
then determine the stars, galaxies and ambiguous sources, based on the dual
selection in both planes (bottom row). These lines and the relevant selections
are described in Section 5.5.1. The DEVILS spectroscopic limit is shown by the
dashed vertical line in the bottom row. Values in the legend are the number of
sources in each category in the D10 region for the full samples, and just to
the DEVILS spectroscopic limits in brackets. $i.e.$ to the DEVILS
spectroscopic limit $\sim 27\%$ of sources are stars and just $\sim 2\%$ are
ambiguous. These numbers are similar in D02 and D03.

The bottom panels of each column in Figure 9 show our final classifications as
either star, galaxy or ambiguous in the D10 region, with the number of sources
in each category displayed in the legend. Within our final catalogues these
classification are delineated by the StarClass value as either 0 (galaxy), 1
(star), or 2 (ambiguous). The same selection is applied to sources in both D02
and D03.

####  5.5.2 Artefact Flags

In addition to flagging of galaxies and stars, we also identify potential
artefacts in our photometry catalogues, where flux is not associated with an
astronomical source, in a similar method to Bellstedt et al. (2020). This is
done in a number of different ways with artefacts flagged as segments that
meet one of the following criteria: i) the source is only detected in one of
the g, r or i bands and not the others; ii) the source is only detected in one
of the z, Y, J, H, Ks bands and not the others; iii) the source’s Y-band R50
is $<0.15^{\prime\prime}$ (smaller than half a pixel); or iv) the source’s r-z
colour is $<-0.75$ mag (unphysical colour). These sources are kept in our
final catalogues but delineated with artefactFlag=1 (all other sources have
artefactFlag=0). As an example of the artefacts in our data, artefactFlag=1
segments are displayed in orange in the left column of Figure 8.

Figure 10: Example of the photometry extraction in the SPIRE 250 band. The top
panel shows the original image, and the middle panel shows the ProFound-
produced model of the objects. The residual when subtracting the model off the
image is shown in the bottom panel.

###  5.6 Differences to the original DEVILS target input catalogue

In Davies et al. (2018) we have already discussed the use of ProFound to
generate the initial spectroscopic targeting catalogue for DEVILS in the NIR.
While our approach is very similar here (but expanded to a broader wavelength
range), there are notable changes designed to make our photometric
measurements more robust. The process outlined in Davies et al. (2018) was
somewhat conservative and designed to produce robust photometry for the bright
(Y$<21.2$ mag) spectroscopic sample for DEVILS at the start of spectroscopic
observations. Here we have improved upon this process in order to both provide
more robust measurements for our spectroscopic input catalogue and to produce
photometric measurements to faint magnitudes for additional science.

Firstly, we now use updated versions of both the UltraVISTA and VIDEO data
that were made available following Davies et al. (2018). Secondly, we have
performed the segmentation fixing outlined in Section 5.1 to fix larger,
extended sources. This was not applied in Davies et al. (2018) and has
improved the photometric measurements for sources at the bright end. Thirdly,
we have improved our masking process based on the experience of Bellstedt et
al. (2020) in producing the GAMA photometry catalogues. Within Davies et al.
(2018) we used a masking procedure based on the ProFound skymean measurements.
This was found to miss some halo regions around bright stars and over-mask
other regions. Our new method improves upon this and reduces the fraction of
the fields that required masking but still removes erroneous sources. Finally,
we also improved our star-galaxy separation method. Previously we only used a
simple NIR colour separation between stars and galaxies. While this works well
for bright systems (which we are targeting spectroscopically) it fails at
fainter magnitudes, hence we apply a more complex method here. However, it is
worth noting that these methods are essentially identical at the bright
(Y$<21.2$ mag) end.

While there are significant changes between the method outlined in Davies et
al. (2018) and the method used here, this has in fact very little impact on
the photometry for our spectroscopic sample (modulo the addition of some new
un-masked objects). The spread ($\sigma$) of Y-band photometric difference
between the target catalogue outlined in Davies et al. (2018) and that
described here is $\sim 0.1$ mag. However, in order to use the best available
photometry for our spectroscopic sample, we now update to our new photometry
and have transitioned to the new input catalogue for our observations based on
the catalogues discussed here.

Table 4: ProFound Options used for FIR photometry for each instrument. For details of each of these parameters and how they affect source detection, see Bellstedt et al. (2020). ProFound Option | MIPS 24/70 | PACS | SPIRE
---|---|---|---
fit$\\_$iters | 5 | 5 | 5
pixcut | 3 | 3 | 3
skycut | 2 | 2 | 2
ext | 1 | 1 | 1
redosky | FALSE | FALSE | FALSE
psf$\\_$redosky | TRUE | TRUE | TRUE
iters | 4 | 4 | 4
tolerance | 1/0 | 0 | 0
sigma | 2 | 2 | 0
magdiff | 5 | 5 | 5

##  6 MIR-FIR Photometric Measurements

Once our UV-MIR catalogues and flags are in place, we then extract photometry
in the MIR-FIR using, once again, a similar method to that of Bellstedt et al.
(2020). Due to the shallower depth, poorer resolution and the existence of FIR
bright sources that are undetected in the optical-NIR, it is not appropriate
to directly apply our NIR-defined segmentation map to the MIR-FIR data in a
similar manner to the shorter wavelength bands. To overcome this, ProFound has
an inbuilt profoundFitMagPSF function specifically defined for the measurement
of photometry in low-resolution, shallow data ($i.e.$ the MIR-FIR). This
method uses the known source positions, here provided from our optical-NIR
catalogues, to fit the PSF of each MIR-FIR band to the images, iterating by
applying an expectation maximisation function. This process is described
extensively in Bellstedt et al. (2020) but briefly, in each band independently
the function extracts flux at the positions of an object catalogue, and then
iterates over the flux of each object to ensure that all non-sky flux is
accounted for in the image and sources are not over extracted. For each band,
the locations of our NIR-selected objects provide the coordinates at which to
place a PSF and extract flux in the longer wavelength data. However, it is
inappropriate to include all of our NIR-selected sources to faint magnitudes
becasue i) Y-flux is not well correlated with the expected emission at longer
wavelengths and ii) the lower resolution of the MIR-FIR data means that
sources will be heavily confused with multiple input galaxies occupying the
same resolution element. As such, we must first apply appropriate cuts to out
NIR-selected catalogue to provide sensible input positions for our MIR-FIR
analysis.

For this, we first run profoundFitMagPSF on the MIPS24 images, using a
Y$<$21.2 mag sample from our UV-MIR catalogue, excluding masked objects and
objects classified as stars or artefacts (for example, this results in 17,209
objects for D10). profoundFitMagPSF not only measures source photometry for
the input sample, but also identifies any additional sources for flux from
objects not aligned with the input catalogue ($i.e.$ NIR-faint systems). In
the MIPS24, a large fraction of sources (28% in D10) are not associated with a
Y$<$21.2 mag source, these sources are then matched back to the full UV-MIR
catalogue using both on-sky positional offset (with a maximum matching radius
of 5′′) and expected Y-24$\mu$m colour. To estimate the expected Y-24$\mu$m
colour we take all Y$<$21.2 mag matches and find the typical Y-24$\mu$m colour
to be 3 mag. We then perform a 3D spherical match between MIPS24 sources and
our UV-MIR catalogue using on-sky separation and (Y-24$\mu$m)-3 colour offset.
$i.e.$ for each UV-MIR source within 5′′ of a MIPS24-detected position, we
assign a probability of correct match based on the sky separation and the
expected Y-24$\mu$m colour, and take the most likely match. As such, we
implicitly assume that all MIPS24 sources are contained within our UV-MIR
catalogue, and that all sources should have the typical Y-24$\mu$m colour.
This process will potentially fail for AGN-like sources which may have
atypical Y-24$\mu$m colours, but these are likely to only form a small
fraction of the source sample. We note that not all MIPS24-detected sources
are matched to a UV-MIR catalogue source using this method, these are retained
as true NIR-faint and MIPS24-bright sources.

To build our input source positions for longer wavelengths, we combine our
Y$<$21.2 mag sample with the MIPS24-detected matched sample as described above
and the additional sources which are unmatched in the UV-MIR catalogue. Using
these input objects, we run profoundFitMagPSF on all of the 70-500 $\mu$m
bands. To account for potential previous over-subtraction of the sky in the
data reduction phase of the FIR imaging by the original teams (possibly due to
the confused nature of sky pixels), we also include a second phase of
extraction with an explicit sky subtraction using the sky value as measured by
ProFound in the first phase.

For completeness the profoundFitMagPSF settings used in our analysis are shown
in Table 4. profoundFitMagPSF accepts an empirical PSF provided by the user in
various forms. Here we use the observed PSF kernels from PEP for the PACS
bands999http://www.mpe.mpg.de/resources/PEP/DR1_tarballs/readme_PEP_global.pdf,
and a Gaussian PSF for the SPIRE bands using the FWHM as given in Oliver et
al. (2012) (18.15, 25.15, 36.3 arcsec for the 250, 350 and 500 $\mu$m bands,
respectively). For the MIPS bands, we use the 100 K PSFs from Gordon et al.
(2008) and re-scale the PSF to the image pixel scale. While measuring flux, we
apply multiplicative aperture corrections of 1.15 for MIPS 70 (Frayer et al.,
2009), 1.50 for PACS 100 and 1.477 for PACS 160, and an additional
multiplicative high-pass correction of 1.12 and 1.11 for PACS 100 and 160,
respectively, as suggested by the PACS data release9.

The fraction of Y$<$21.2 mag objects with detections (arbitrarily defining a
detection as having a measured magnitudes of $<30$ mag) varied in each band
depending on the depth of the imaging. For example, in D10 the
$M24/M70/P100/P160/S250/S350/S500$ $\mu$m bands,
$81.1/44.4/55.8/57.1/62.5/44.4/28.3$ per cent of objects had detections,
respectively. 1494 objects had detections in all seven bands, corresponding to
8.6 per cent of the Y$<$21.2 mag sample. No objects were undetected in all of
the seven bands.

The resulting uncertainties, due to the expectation maximisation mixture
modelling process, accurately reflect the inherent uncertainties in this
process. If two optically-detected sources are close in projection, then the
uncertainty will reflect the potential confusion between these two sources. In
Figure 10 we show an example of an original image, final modelled image, and
resulting residual in the SPIRE 250 $\mu$m band in D10.

In addition to the objects detected in the FIR with optical/MIR counterparts,
a small fraction of FIR objects were detected with no clear counterpart - i.e.
objects which are not detected in MIPS24 but are detected at longer
wavelengths - likely high redshift FIR-bright systems. The outputs for these
additional sources are saved, but not included in our DEVILS photometry
catalogue (as they are not associated with a DEVILS target). However, we do
add these sources into the catalogues used to generate the deep number counts
presented in this work.

Figure 11: Comparison between our new ProFound photometry and COSMOS2015
catalogue. Each panel displays ProFound-COSMOS2015 for 2′′ matched sources.
Note that in r, i and z bands we compare HSC with Subaru SuprimeCam, and in
VISTA Y, J, H and Ks bands we compare UltraVISTA DR4 with DR2. Solid and
dashed lines show the running median and interquartile range of the data,
respectively. The full sample median and ranges are noted in each panel. Note
that these do not have MW extinction corrections applied to be comparable to
the public COSMOS2015 catalogue. Note that COSMOS2015 does not provide g-band
measurements, and as such no comparison is shown here. Note, the diagonal
feature in the i-band for both this figure and Figure 12 are cause by
saturated sources in the old SuprimeCam data used in both COSMOS2105 and
G10/COSMOS. Figure 12: Same as Figure 11 but for the G10/COSMOS catalogue.
However, these have MW extinction corrections applied to be comparable to the
public G10/COSMOS photometry. Note that in r, i and z bands we compare HSC
with Subaru SuprimeCam, and in VISTA Y, J, H and Ks bands we compare
UltraVISTA DR4 with DR2

Figure 13: Same as Figure 11 but showing comparisons between our new MIR-FIR
ProFound photometry and COSMOS2015 (blue), G10/COSMOS (red) and XID+ (green)
catalogues, where available, in the MIR-FIR regime.

##  7 Verification of Photometry

In the following section we carry out a number of verification checks to
assess the validity of our current photometry catalogue. Here we present these
comparisons just in the D10 region, where more extensive existing data is
available, to highlight the validity of our process. However, similar checks
are performed on all regions with similar results. These tests are intended to
provide the reader with the confidence that our new photometry is robust, and
can be used for future science projects.

###  7.1 Comparisons to Previous Photometry in the COSMOS Field

First we compare our total photometry measurements to existing photometry
catalogues in the COSMOS region. We do not assume any particular catalogue is
the ground truth, but simply highlight the consistency (or lack thereof)
between photometric measurements using different techniques.

In Figure 11 we compare our UV-MIR photometric measurements with those from
the COSMOS2015 catalogue of Laigle et al. (2016). Briefly, Laigle et al.
(2016) uses SExtractor (Bertin & Arnouts, 1996) to derive total and fixed
aperture photometry from the Optical-NIR multi-wavelength data in COSMOS.
These are then catalogue matched to the UV and longer wavelength data
available in the region - please refer to Laigle et al. (2016) for further
details. Here we first compare to the total (MAG_AUTO) measurements where
available. We note that the imaging used in this work and Laigle et al. (2016)
catalogue is identical except for the following: we have used newer HSC data
in g, r, i, z bands in comparison to the Subaru SuprimeCam data used in
COSMOS2015, and we use UltraVISTA DR4 while COSMOS2015 uses UltraVISTA DR2. In
Figure 11 we also compare photometry prior to galactic extinction correction
being applied, as this is how the raw COSMOS2015 photometry is provided. We
find only very small median offsets in the majority of bands ($<$0.1 mag),
showing consistency between our measurements, with the exception of the GALEX
bands that show a normalisation offset of $\sim 0.25-0.3$ mag. This is
discussed further in Section 7.2. However, we find that our photometry and
that derived in COSMOS2015 is largely consistent despite using different
methods, and in some cases, different data sets. However, our new photometry
will not suffer from some of the issues discussed previously (and extensively
in Robotham et al., 2018) and is more robust for extracting total photometry
measurements.

Next we compare our UV-MIR photometry to the G10/COSMOS catalogue of Andrews
et al. (2017) in Figure 12. The G10/COSMOS catalogue uses the Lambda Adaptive
Multi-wavelength Analysis for R (LAMBDAR, Wright et al., 2016) code with
identical imaging datasets to COSMOS2015. The LAMBDAR software starts with
base apertures defined by SExtractor, but within Andrews et al. (2017) they go
through an extensive processes of manually fixing any erroneous apertures.
Firstly, in Figure 12 we see comparable median offsets to the COSMOS2015
catalogue in the GALEX UV bands, once again this is discussed further in
Section 7.2. Through the u-Ks bands, we see normalisation offsets of up to
$\sim$0.3 mag, particularly in the VISTA NIR bands. These are consistent with
the offsets between G10/COSMOS and COSMOS2015 presented in Andrews et al.
(2017). In Andrews et al. (2017) it was argued that these offsets are likely
due to the initial aperture definitions and/or choice of selection band.
However, given that our new ProFound photometry does not rely on apertures for
photometry, and we agree more closely with the COSMOS2015 catalogue in the
NIR, this is unlikely to be the case and the G10/COSMOS measurements are
simply missing flux and/or have a zero-point error. We do agree very well with
IRAC MIR measurements from G10/COSMOS with $<0.1$ mag median offset in all
bands.

For our MIR-FIR photometry, we again compare to COSMOS2015 and G10/COSMOS, but
also to the XID+ SPIRE catalogues of the $Herschel$ Extragalactic Legacy
Project (HELP) team (see Hurley et al., 2017). All comparisons are presented
in Figure 13. We find good agreement with all existing photometry in the
Spitzer and, $Herschel$ PACS and SPIRE data - suggesting our photometry is
consistent with existing catalogues. We highlight that while we are consistent
with these existing catalogue across their individual regimes, the benefit
here is that we are using a single consistent package to extract photometry
across the full wavelength range, removing some of the issues associated with
catalogue matching of data. We have also optimised our catalogues to robustly
measure total flux measurements across all sources, which are required for
robust measurements of galaxy properties such as SFRs and stellar masses (see
Thorne et al., 2020).

###  7.2 Photometric Offsets

As discussed in the previous section, while our photometric measurements are
largely consistent with existing catalogues we do see a number of small median
zero point offsets. These are most significant in the GALEX-UV bands. However,
the methods used to calculate the photometry in these bands is vastly
different between our new work and the existing catalogues, and a detailed
assessment of the previous photometry finds a number of seemingly arbitrary
zero-point corrections are applied to the photometry in these bands. As such,
we do not know which catalogue now presents the ground truth.

To test this, we use the ProSpect (Robotham et al., 2020) spectral energy
distribution (SED) fitting results outlined in Thorne et al. (2020). Thorne et
al fit all DEVILS D10 galaxies using our new photometry to produce a robust
SED fit and derive star-formation histories, stellar masses, SFRs, etc. For
our tests we then also fit the existing COSMOS2015 photometry (which shows the
similar offsets as G10/COSMOS - particularly in the UV bands) for a random
sample of $\sim 24,000$ DEVILS galaxies with spectroscopic redshifts at $z<1$,
and using the same ProSpect parameters as Thorne et al. Here we use the best-
fit ProSpect SED and compare to the input photometric measurements, for both
ProFound total photometry and the COSMOS2015 photometry. For COSMOS2015 we use
the 2′′ aperture photometry measurements to maximise robust SED shape fits,
$i.e.$ aiming to obtain the best possible fits. We also note here that the
COSMOS2015 sample already apply per-band zero-point offsets using the LePhare
SED fitting code in order to improve the accuracy of their photometric
redshifts. As such, their photometry should be tuned to closely match the
overall SED shape of existing galaxy templates. However, systematic offsets
between the ProSpect fit and input photometry can highlight potential zero-
point offsets in a given photometric band, assuming the suite of SED models is
representative of the true galaxy population. Figures 14 and 15 display the
input photometric measurement minus the ProSpect best-fit SED measurements for
all bands. In each panel we note the median offset of each distribution, the
offset from the peak of a Gaussian fit to the distribution and the peak
position offset for both photometry catalogues as a measure of any systematic
differences between the photometric catalogues and ProSpect SED models. We
find no significant residual offset between our ProFound photometry and the
ProSpect best-fit SED in the GALEX bands, or in fact any others. There is a
slight offset in the HSC g-band ($\sim 0.07$ mag), which we believe is due to
the 4000Å break falling within this band at the redshift of a typical DEVILS
galaxy and the ProSpect models not completely encompassing the true range of
galaxy types at this point. When the full Thorne et al. (2020) sample
(including photometric redshifts) is fit using a broader redshift range, this
offset disappears. However, we do see numerous offsets for the COSMOS2015
photometry when fitted using ProSpect, in multiple bands in Figure 14 in the
NUV, z, H, Ks and IRAC channels. This suggests that our new photometry is more
consistent with realistic galaxy SEDs (at least those probed by ProSpect). We
also display the outlier fraction, defined as the fraction of sources that are
outside the offset peak value from ProFound/COSMOS2015 $\pm$ 2 $\times$ the
standard deviation of the ProFound distribution. We find that the COSMOS2015
photometry has larger outlier fractions in almost all bands (modulo u, i, Y
and S500 where the offsets are comparable). This further suggests that
measured scatter in our new photometry is smaller when compared to the
ProSpect SED models. In combination, these results suggest that any
differences in zero point between our new photometry and the COSMOS2015
catalogues is unlikely to be due to errors in our current approach.

Figure 14: Individual band offsets between measured photometry for a random
sample of 24,000 D10 galaxies and the ProSpect best fit SED in FUV - Ks.
Coloured filled histograms and right-hand legend show our new photometry. Grey
lines and left hand legend show COSMOS2015 photometry. In the legends we
display the offset values measured in three different ways, where large
absolute values indicate a larger discrepancy with the ProSpect SED models
(see text for details). In most bands our new photometry has smaller offsets
using all methods. We also show the outlier fraction (see text for details),
where our new photometry has comparable or smaller values in every band. The
dashed vertical line display the zero offset line, while the dotted green line
shows the peak of the ProFound-derived distribution.  Figure 15: The same as
Figure 14 but for the MIR-FIR (S36 - S500) bands.  Figure 16: Comparison of
colour histograms in adjacent filters for a matched sample of D10 ProFound,
COSMOS2015 and G10/COSMOS galaxies. The left panel displays the interquartile
range and right panel the outlier fraction. A smaller value in each of these
figures is desirable.

###  7.3 Colour Comparisons

While the figures displayed in previous sections are useful for diagnosing
zero-point and linearity problems ($i.e.$ magnitude-dependent offsets), they
do not give an objective assessment as to which data set is more robust in
terms of the photometry measurement process. One potential avenue to explore
this is through galaxy colours in adjacent filters. A sample of galaxies will
have some intrinsic colour distribution, which is then convolved with error
distributions introduced by the instrumentation, observing conditions,
photometric data reduction methods and photometric measurement error. A
narrower colour distribution and a lower outlier fraction thus indicating a
narrower photometric error distribution are, hence, likely more reliable.

To explore the colour distributions of our sample we first measure the colours
in adjacent bands for a 2′′ matched sample of Y$<$24.5 mags galaxies in the
D10 region, for FUV-S80, and sources brighter than the 5$\sigma$ detection
limit, for MIPS24-S500, for ProFound, COSMOS2015 and G10/COSMOS. Here we use
the colour-optimised photometry from ProFound and fixed 2′′ (colour-optimised)
photometry from COSMOS2015 (for G10/COSMOS only total photometry is provided).
For each distribution we measure the interquartile range and outlier fraction
(defined as the number of sources which are $>$0.5 mag outside of the
interquartile range) for each catalogue and in each band.

Rather than showing the raw distributions, to better illustrate these
comparisons, Figure 16 displays the interquartile range and outlier fraction
for all colours, where a smaller value in each of these figures is desirable.
Our new ProFound photometry does better (or comparable) to both existing
datasets in all colours at FUV-160 $\mu$m wavelengths. We do see a
significantly larger outlier fractions in our FIR SPIRE measurements, which
are consistent with the ProSpect residual distributions shown in Figure 15,
where the COSMOS2015 photometry displays a more peaked distribution in the
FIR. We have explored this discrepancy in a number of ways, and have been
unable to improve the tightness of the colour distributions in our SPIRE
photometry. However, for the sake of consistency between both our multi-
wavelength photometry and the approach used in GAMA, we opt to retain our
method for deriving the FIR photometry. We also note that only a small
fraction of the DEVILS spectroscopic sample ($\sim 15\%$) have a detection in
all of the SPIRE bands, and as such the DEVILS sample is unlikely to be
significantly affected by these small differences in photometry.

Figure 17: Comparison of the physical properties derived using ProSpect for a
common sample of galaxies but using both the COSMOS2015 photometry and our new
ProFound photometry. We split the sample into three redshift bins between
$0.2<z<0.8$. The top row displays the difference between stellar masses
derived using the COSMOS2015 and ProFound photometry. Middle row show the same
but for SFRs, while the bottom row shows the full sSFR-M∗ relation for this
sample. Stellar masses are consistent but with a 0.1-0.2 dex offset and the
ProFound photometry produces systematically larger SFR which goes with sSFR.
Combined this would lead to a distinctly different measurement of the
evolution of the sSFR-M∗ relation at M${}^{*}<10^{10}$M⊙.

###  7.4 Impact on derived physical properties

Following on from the above residuals and colour comparisons, it is also
beneficial to assess how our new photometry catalogue impacts the derived
properties of galaxies, when removing any other differences in the method for
measuring these properties ($i.e.$ how does the photometry alone change our
galaxies physical properties and hence scientific conclusions). To do this we
compare the SED-derived properties of a subsample of galaxies using the same
method, but differing photometric measurements.

While we leave a full analysis of the DEVILS ProSpect stellar mass and SFR
measurements to Thorne et al. (2020), Figure 17 shows a comparison of the
ProSpect-derived stellar masses and sSFR produced using the analysis/sample
outlined in Section 7.2 ($i.e$ for an identical fitting procedure with
ProSpect but using both the COSMOS2015 photometry and our new ProFound
photometry for an identical set of galaxies). We split the sample into three
redshift bins between $0.2<z<0.8$ (see Thorne et al., 2020, for a full
description of this sample and redshifts) and display the difference between
the resultant ProFound and COSMOS2015 stellar masses (top row), SFRs (middle
row) and along the sSFR-M∗ relation (bottom row). We find that derived stellar
masses are consistent between the two samples, but with a systematic
0.1-0.2dex offset, but SFRs show a systematic bias to higher SFRs in ProFound
in particular galaxy populations. This effect becomes more pronounced as we
move to higher redshifts. This is most clearly apparent in the comparison of
the sSFR-M∗ relation where our ProFound and COSMOS2015 photometry produce a
comparable relation at low redshift (median and scatter in sSFR are consistent
at all stellar masses), but the samples begin to diverge significantly for
M${}^{*}<10^{10}$M⊙ galaxies at higher redshifts. Importantly this shows that
only changing the photometric measurements for relatively bright galaxies, can
have a significant impact on the measured evolution of the sSFR-M∗ relation
and associated conclusions. This just represents a single example where
different photometric measurements can strongly impact derived scientific
conclusions.

While it is still difficult to assess which sample would be deemed ‘correct’
through this analysis, we note that through the photometry measurements alone,
a larger stellar mass and SFR (as is the case systematically from ProFound),
suggests that one is measuring more true flux from the system, and therefore
more robustly capturing the total emission from the galaxy. It is somewhat
difficult to get the converse (systematically lower measurements) erroneously
when only considering photometric measurement differences for a broad
population of galaxies across a range of epochs. This, coupled with the
analysis in Section 7.2, suggests that our new ProFound measurements are
robustly capturing the total galaxy flux across a range of wavelengths, and
thus can be used to measure galaxy properties ($i.e.$ see Thorne et al., 2020)

###  7.5 Astrometric Accuracy

Finally, to verify the astrometric accuracy of our catalogue, we perform a
$2^{\prime\prime}$ match to $12<g_{\mathrm{GAIA}}<18$ mag sources from the
GAIA DR2 catalogues (Gaia Collaboration et al., 2018; Lindegren et al., 2018).
Figure 18 displays the R.A. and Dec offsets with the median offset marked as
the blue cross. Circles indicate the UltraVISTA/VIDEO pixel size and typical
seeing, showing that our final catalogues have sub-pixel astrometric accuracy
in comparison to GAIA in D10. While we do see small ($\sim$1 pixel) offsets in
D02/D03 we do not opt to correct for this astrometry mis-match. Instead we
retain our catalogues with the astrometry derived from the DEVILS input
catalogue photometry ($i.e.$ derived from the UltraVISTA and VIDEO data
directly). We note that the VIDEO data we use here has not been aligned with
GAIA, but newer iterations and public releases will be.

Figure 18: Astrometric accuracy of new catalogues against GAIA DR2 in each of
the DEVILS regions (Top= D10, Middle=D02, Bottom=D03). The offsets in D02/D03
are driven by offsets in the VIDEO data astrometry.

Figure 19: Deep galaxy number counts. We show each field individually for D02
(gold), D03 (red) and D10 (green), and for the full DEVILS sample (blue).
Error bars (for Poisson+cosmic variance errors) are plotted, but in most cases
are smaller than the data point. We also show the compendium of data from
Driver et al. (2016a) as faint grey points. The left panel displays the number
counts directly, while the right panel shows the number counts with a spline
fit to the data removed (grey dashed line, see Section 8 for details). Also
shown are the nominal survey quoted depth (black vertical dashed) and measured
galaxy completeness depth for each individual field and the full sample
(dashed vertical coloured lines). These depths are also noted in the left
panel legend and Table 1.

Figure 20: Figure 19 continued.

Figure 21: Figure 19 continued.

Figure 22: Figure 19 continued.

##  8 Deep Number Counts

While our photometric catalogues are produced primarily to allow scientific
analysis in a number of areas, they also provide some of the deepest
photometric catalogues consistently measured over a broad wavelength range and
over $>$1 deg2 area scales. Here we use these catalogues to produce deep
galaxy number counts as an additional photometric validation, for the purposes
of exploring the overall galaxy population in broader studies, and for tuning
numerical simulations of the galaxy distribution. These number counts have
recently been used in Koushan et al. (2021) to measure the Extragalactic
Background Light (EBL) from the UV-MIR. Hence, we do not go into detail
regarding the scientific implications of these distributions, but simply
produce them and make available to the wider community.

Figures 19, 20, 21 and 22 display the number of galaxies in our unmasked
regions per unit sky area (left column). We take all sources identified as
galaxies using our selections described in Section 5.5.1, and bin in 0.5 mag
ranges in each band and DEVILS field independently. Here we include both our
NIR-selected catalogues and, in the FIR, include the ‘extra sources’
identified using ProFound (described in Section 6). We display the number
counts for each DEVILS region independently as small gold, red and green
circles and the combined counts for the full DEVILS sample as the larger blue
circles. Error bars are derived from the Poisson error combined linearly with
cosmic variance errors, estimated using the method outlined in Driver &
Robotham (2010). In most cases, these error bars are smaller than the data
points. For comparison, we also display the compendium of photometric data
from Driver et al. (2016a), and find remarkable agreement in the UV-MIR but
now with smaller errors (the Driver et al. points essentially use just the
COSMOS region at these depths and thus have larger Poisson and cosmic variance
errors).

On these figures we also display the nominal quoted depth of the input imaging
as the dashed vertical black lines (also given in Table 1), and the measured
depth to galaxies in each field individually and for the full sample as the
vertical coloured lines. These values are calculated as the point where each
of the number count distributions turn over. These limits are displayed in the
right column of these Figures and also in Table 1.

In order to highlight the small differences between each DEVILS region (and
the literature values), in the right column of Figures 19, 20, 21 and 22 we
also show these number counts with a 10th order spline fit removed. The spline
is fit to combined counts for the full DEVILS sample (blue circles) at
magnitudes below the combined counts depth (vertical blue dashed lines). At
fainter magnitudes we use the literature values from Driver et al. (2016a). We
note that the choice of 10 degrees of freedom is largely arbitrary, but that
changing this value does not significantly change our results.

We note that our deep number counts are measured consistently across all
bands, with only small differences between fields at faint magnitudes. These
counts extend to faint magnitudes over a large area $\sim$6 deg2, making them
the state-of-the-art in using galaxy number counts to explore the Universal
distribution of flux at a given wavelength (Koushan et al., 2021). We
therefore provide these for use in the larger community in a tabulated form in
Table 5.

Table 5: Tabulated number densities of sources in each photometric band.
Magnitude is the centre of a $\Delta$0.5mag bin. All other values are N(m),
0.5 mag-1 deg-2. These are also made available in a machine-readable form as
online data products associated with this paper.

Mag. | FUV | NUV | u | g | r | i | z | Y | J | H
---|---|---|---|---|---|---|---|---|---|---
10.0 | - | - | - | - | - | - | - | - | - | -
10.5 | - | - | - | - | - | - | - | - | - | -
11.0 | - | - | - | - | - | - | - | - | - | -
11.5 | - | - | - | - | - | - | - | - | - | -
12.0 | - | - | - | - | - | - | - | - | - | -
12.5 | - | - | - | - | - | - | - | - | - | -
13.0 | - | - | - | - | - | - | - | - | - | -
13.5 | - | - | - | - | - | - | - | - | - | -
14.0 | - | - | - | - | - | - | - | - |  0.3$\pm$0.6 |  0.3$\pm$0.6
14.5 | - | - | - |  0.2$\pm$0.4 |  0.2$\pm$0.4 |  0.2$\pm$0.4 |  0.4$\pm$0.7 |  0.5$\pm$0.7 |  0.5$\pm$0.8 |  2.3$\pm$1.7
15.0 | - | - | - |  0.3$\pm$0.6 |  0.5$\pm$0.8 |  0.7$\pm$0.9 |  1.3$\pm$1.3 |  2.5$\pm$1.8 |  5.7$\pm$2.9 |  8.1$\pm$3.5
15.5 | - | - | - |  0.8$\pm$1.0 |  1.0$\pm$1.1 |  4.5$\pm$2.5 |  6.5$\pm$3.1 |  11.7$\pm$4.4 |  14.4$\pm$5.0 |  19.3$\pm$6.0
16.0 | - | - | - |  0.8$\pm$1.0 |  7.0$\pm$3.2 |  12.2$\pm$4.5 |  17.4$\pm$5.6 |  21.9$\pm$6.5 |  30.0$\pm$7.9 |  32.6$\pm$8.4
16.5 | - | - | - |  3.9$\pm$2.3 |  14.3$\pm$4.9 |  25.3$\pm$7.1 |  29.7$\pm$7.9 |  35.2$\pm$8.8 |  49.0$\pm$11.0 |  68.8$\pm$13.9
17.0 | - | - |  0.7$\pm$0.9 |  11.6$\pm$4.3 |  27.5$\pm$7.5 |  39.1$\pm$9.4 |  56.2$\pm$12.1 |  73.9$\pm$14.6 |  101$\pm$18 |  131$\pm$22
17.5 |  0.2$\pm$0.4 |  0.2$\pm$0.4 |  3.7$\pm$2.2 |  19.1$\pm$5.9 |  44.6$\pm$10.3 |  84.4$\pm$16.1 |  109$\pm$19 |  135$\pm$23 |  171$\pm$27 |  227$\pm$34
18.0 |  0.5$\pm$0.8 |  1.2$\pm$1.2 |  8.2$\pm$3.5 |  38.2$\pm$9.3 |  91.4$\pm$17.0 |  141$\pm$23 |  188$\pm$29 |  227$\pm$34 |  297$\pm$41 |  386$\pm$51
18.5 |  1.9$\pm$1.5 |  3.2$\pm$2.1 |  15.1$\pm$5.1 |  55.7$\pm$12.0 |  155$\pm$25 |  247$\pm$36 |  328$\pm$45 |  386$\pm$51 |  494$\pm$62 |  662$\pm$80
19.0 |  4.8$\pm$2.6 |  7.6$\pm$3.4 |  30.7$\pm$8.0 |  107$\pm$19 |  258$\pm$37 |  403$\pm$53 |  539$\pm$67 |  652$\pm$79 |  837$\pm$97 |  1137$\pm$126
19.5 |  8.6$\pm$3.6 |  13.6$\pm$4.8 |  55.7$\pm$12.0 |  183$\pm$28 |  430$\pm$56 |  668$\pm$80 |  898$\pm$103 |  1109$\pm$124 |  1397$\pm$151 |  1851$\pm$194
20.0 |  16.3$\pm$5.4 |  32.0$\pm$8.3 |  94.3$\pm$17.4 |  291$\pm$41 |  678$\pm$81 |  1071$\pm$120 |  1479$\pm$159 |  1776$\pm$187 |  2283$\pm$234 |  2903$\pm$291
20.5 |  32.5$\pm$8.4 |  53.6$\pm$11.7 |  182$\pm$28 |  476$\pm$61 |  1047$\pm$118 |  1690$\pm$179 |  2358$\pm$241 |  2848$\pm$286 |  3588$\pm$353 |  4437$\pm$428
21.0 |  58.5$\pm$12.4 |  111$\pm$20 |  334$\pm$46 |  751$\pm$89 |  1603$\pm$171 |  2643$\pm$267 |  3643$\pm$358 |  4364$\pm$422 |  5373$\pm$512 |  6371$\pm$599
21.5 |  113$\pm$20 |  217$\pm$32 |  563$\pm$70 |  1202$\pm$133 |  2439$\pm$248 |  4034$\pm$393 |  5455$\pm$519 |  6414$\pm$603 |  7660$\pm$712 |  9044$\pm$833
22.0 |  199$\pm$30 |  393$\pm$52 |  1023$\pm$115 |  1965$\pm$205 |  3739$\pm$366 |  5989$\pm$566 |  8003$\pm$742 |  9370$\pm$861 |  11227$\pm$1022 |  13116$\pm$1184
22.5 |  370$\pm$49 |  737$\pm$87 |  1899$\pm$198 |  3372$\pm$333 |  5944$\pm$562 |  8975$\pm$827 |  11559$\pm$1050 |  13572$\pm$1224 |  16399$\pm$1466 |  19021$\pm$1689
23.0 |  635$\pm$77 |  1317$\pm$144 |  3774$\pm$369 |  5939$\pm$561 |  9542$\pm$876 |  13336$\pm$1203 |  16732$\pm$1494 |  19713$\pm$1748 |  23484$\pm$2069 |  26909$\pm$2359
23.5 |  1130$\pm$126 |  2335$\pm$239 |  7383$\pm$688 |  10798$\pm$985 |  15371$\pm$1378 |  20295$\pm$1798 |  23943$\pm$2108 |  27671$\pm$2423 |  31971$\pm$2787 |  35937$\pm$3121
24.0 |  2002$\pm$208 |  4064$\pm$395 |  13391$\pm$1208 |  18361$\pm$1633 |  24211$\pm$2130 |  29416$\pm$2571 |  33312$\pm$2900 |  36667$\pm$3182 |  40744$\pm$3525 |  43078$\pm$3721
24.5 |  3598$\pm$353 |  6808$\pm$638 |  22224$\pm$1962 |  28471$\pm$2491 |  34965$\pm$3039 |  39707$\pm$3438 |  43335$\pm$3743 |  44337$\pm$3827 |  46237$\pm$3987 |  43808$\pm$3783
25.0 |  6690$\pm$627 |  11025$\pm$1004 |  32556$\pm$2836 |  39336$\pm$3407 |  45305$\pm$3908 |  47169$\pm$4065 |  50110$\pm$4311 |  47111$\pm$4060 |  44187$\pm$3814 |  36890$\pm$3201
25.5 |  10846$\pm$989 |  17263$\pm$1539 |  41211$\pm$3565 |  47325$\pm$4078 |  49256$\pm$4240 |  45640$\pm$3936 |  46263$\pm$3989 |  42220$\pm$3649 |  33304$\pm$2899 |  26126$\pm$2293
26.0 |  14840$\pm$1332 |  24679$\pm$2170 |  43026$\pm$3717 |  45773$\pm$3948 |  41809$\pm$3615 |  34209$\pm$2975 |  32160$\pm$2803 |  26500$\pm$2324 |  20150$\pm$1786 |  16906$\pm$1509
26.5 |  18398$\pm$1636 |  30594$\pm$2670 |  36589$\pm$3176 |  36117$\pm$3136 |  28939$\pm$2531 |  21662$\pm$1914 |  19592$\pm$1738 |  13287$\pm$1199 |  11125$\pm$1013 |  9485$\pm$871
27.0 |  21876$\pm$1932 |  32558$\pm$2836 |  27218$\pm$2385 |  24624$\pm$2165 |  17667$\pm$1574 |  12604$\pm$1140 |  10409$\pm$951 |  6466$\pm$608 |  4966$\pm$476 |  4360$\pm$422
27.5 |  24730$\pm$2174 |  30018$\pm$2622 |  18389$\pm$1636 |  15211$\pm$1364 |  10103$\pm$925 |  6558$\pm$616 |  4650$\pm$448 |  2395$\pm$244 |  1638$\pm$174 |  1877$\pm$196

Mag. | Ks | S36 | S45 | S58 | S80 | M24 | M70 | P100 | P160 | S250 | S350 | S500
---|---|---|---|---|---|---|---|---|---|---|---|---
10.0 | - | - | - | - | - | - | - |  0.7$\pm$0.9 |  0.7$\pm$0.9 |  0.2$\pm$0.4 | - | -
10.5 | - | - | - | - | - | - |  0.2$\pm$0.4 |  2.5$\pm$1.8 |  6.0$\pm$2.9 |  0.5$\pm$0.7 |  0.2$\pm$0.4 | -
11.0 | - | - | - | - | - | - |  0.5$\pm$0.8 |  9.5$\pm$3.9 |  19.1$\pm$5.9 |  3.2$\pm$2.0 |  0.2$\pm$0.4 | -
11.5 | - | - | - | - | - | - |  2.4$\pm$1.7 |  24.1$\pm$6.9 |  65.6$\pm$13.5 |  16.7$\pm$5.5 |  3.7$\pm$2.2 | -
12.0 | - | - | - | - | - | - |  8.7$\pm$3.7 |  61.6$\pm$12.9 |  261$\pm$37 |  87.7$\pm$16.5 |  37.3$\pm$9.2 |  5.7$\pm$2.9
12.5 | - | - | - | - |  0.2$\pm$0.4 |  0.2$\pm$0.4 |  21.1$\pm$6.3 |  179$\pm$28 |  661$\pm$80 |  319$\pm$44 |  177$\pm$28 |  48.9$\pm$11.0
13.0 | - | - | - | - |  0.2$\pm$0.4 |  0.5$\pm$0.8 |  42.5$\pm$10.0 |  508$\pm$64 |  921$\pm$105 |  801$\pm$94 |  521$\pm$65 |  208$\pm$31
13.5 | - | - | - | - |  0.8$\pm$1.0 |  1.4$\pm$1.3 |  119$\pm$21 |  808$\pm$94 |  1026$\pm$116 |  1371$\pm$149 |  979$\pm$111 |  492$\pm$62
14.0 |  0.3$\pm$0.6 | - |  0.2$\pm$0.4 |  0.2$\pm$0.4 |  2.0$\pm$1.6 |  2.7$\pm$1.9 |  330$\pm$45 |  871$\pm$101 |  1004$\pm$114 |  1708$\pm$181 |  1265$\pm$139 |  743$\pm$88
14.5 |  2.2$\pm$1.7 |  0.3$\pm$0.6 | - |  0.3$\pm$0.6 |  6.8$\pm$3.2 |  10.0$\pm$4.0 |  782$\pm$92 |  912$\pm$105 |  729$\pm$86 |  1533$\pm$164 |  1183$\pm$131 |  818$\pm$95
15.0 |  7.9$\pm$3.5 |  0.5$\pm$0.7 |  0.5$\pm$0.7 |  1.2$\pm$1.2 |  15.2$\pm$5.1 |  20.6$\pm$6.2 |  1084$\pm$122 |  819$\pm$95 |  472$\pm$60 |  1157$\pm$128 |  899$\pm$103 |  673$\pm$81
15.5 |  19.8$\pm$6.1 |  5.0$\pm$2.7 |  1.3$\pm$1.3 |  2.9$\pm$1.9 |  33.6$\pm$8.5 |  41.8$\pm$ 9.9 |  1071$\pm$120 |  581$\pm$71 |  290$\pm$41 |  747$\pm$88 |  577$\pm$71 |  485$\pm$62
16.0 |  39.0$\pm$9.4 |  14.1$\pm$4.9 |  7.7$\pm$3.4 |  8.8$\pm$3.7 |  54.8$\pm$11.9 |  98.6$\pm$18.0 |  767$\pm$90 |  367$\pm$49 |  160$\pm$26 |  450$\pm$58 |  399$\pm$53 |  334$\pm$45
16.5 |  89.7$\pm$16.8 |  32.6$\pm$8.4 |  21.4$\pm$6.4 |  19.1$\pm$5.9 |  88.4$\pm$16.6 |  239$\pm$35 |  450$\pm$58 |  224$\pm$33 |  105$\pm$19 |  308$\pm$43 |  277$\pm$39 |  262$\pm$38
17.0 |  163$\pm$26 |  62.8$\pm$13.0 |  48.7$\pm$11.0 |  49.2$\pm$11.0 |  149$\pm$24 |  573$\pm$71 |  318$\pm$44 |  135$\pm$23 |  66.5$\pm$13.6 |  195$\pm$30 |  207$\pm$31 |  188$\pm$29
17.5 |  288$\pm$40 |  139$\pm$23 |  106$\pm$19 |  90.1$\pm$16.8 |  221$\pm$33 |  1355$\pm$148 |  227$\pm$34 |  80.5$\pm$15.5 |  46.0$\pm$10.5 |  125$\pm$21 |  140$\pm$23 |  140$\pm$23
18.0 |  505$\pm$64 |  310$\pm$43 |  224$\pm$33 |  175$\pm$28 |  333$\pm$45 |  1769$\pm$187 |  160$\pm$26 |  45.2$\pm$10.4 |  25.6$\pm$7.2 |  78.5$\pm$15.3 |  84.2$\pm$16.0 |  90.9$\pm$17.0
18.5 |  885$\pm$102 |  702$\pm$84 |  474$\pm$60 |  382$\pm$51 |  519$\pm$65 |  1970$\pm$205 |  103$\pm$19 |  28.1$\pm$7.6 |  15.9$\pm$5.3 |  39.0$\pm$9.4 |  49.2$\pm$11.0 |  53.1$\pm$11.6
19.0 |  1544$\pm$165 |  1578$\pm$168 |  1084$\pm$121 |  872$\pm$101 |  812$\pm$95 |  1704$\pm$180 |  64.3$\pm$13.3 |  12.6$\pm$4.6 |  8.0$\pm$3.5 |  28.5$\pm$7.7 |  35.7$\pm$8.9 |  37.0$\pm$9.1
19.5 |  2442$\pm$249 |  2937$\pm$294 |  2399$\pm$245 |  1857$\pm$195 |  1475$\pm$159 |  978$\pm$111 |  34.2$\pm$8.6 |  12.4$\pm$4.5 |  5.9$\pm$2.9 |  17.9$\pm$5.7 |  22.1$\pm$6.5 |  20.8$\pm$6.2
20.0 |  3792$\pm$371 |  4594$\pm$443 |  4333$\pm$419 |  3670$\pm$360 |  2841$\pm$285 |  632$\pm$77 |  23.0$\pm$6.7 |  7.0$\pm$3.2 |  2.7$\pm$1.9 |  12.9$\pm$4.6 |  14.7$\pm$5.0 |  14.1$\pm$4.9
20.5 |  5435$\pm$518 |  6415$\pm$603 |  6479$\pm$609 |  6549$\pm$615 |  5539$\pm$526 |  409$\pm$54 |  12.8$\pm$4.6 |  5.0$\pm$2.7 |  2.3$\pm$1.7 |  9.2$\pm$3.8 |  10.0$\pm$4.0 |  13.2$\pm$4.7
21.0 |  7471$\pm$697 |  8449$\pm$781 |  8654$\pm$799 |  10931$\pm$996 |  9961$\pm$912 |  244$\pm$36 |  9.7$\pm$3.9 |  1.7$\pm$1.4 |  0.7$\pm$0.9 |  9.5$\pm$3.9 |  8.0$\pm$3.5 |  10.5$\pm$4.1
21.5 |  10410$\pm$953 |  11104$\pm$1011 |  11900$\pm$1080 |  16357$\pm$1462 |  15866$\pm$1420 |  156$\pm$25 |  7.0$\pm$3.2 |  1.5$\pm$1.4 |  1.3$\pm$1.3 |  8.7$\pm$3.7 |  9.7$\pm$3.9 |  10.0$\pm$4.0
22.0 |  14741$\pm$1326 |  15157$\pm$1359 |  17134$\pm$1528 |  21138$\pm$1870 |  20990$\pm$1857 |  97.1$\pm$17.8 |  9.2$\pm$3.8 |  1.7$\pm$1.4 |  0.7$\pm$0.9 |  10.0$\pm$4.0 |  16.2$\pm$5.4 |  10.0$\pm$4.0
22.5 |  20908$\pm$1853 |  21886$\pm$1933 |  23843$\pm$2099 |  23849$\pm$2100 |  23591$\pm$2078 |  57.1$\pm$12.2 |  10.5$\pm$4.1 |  1.2$\pm$1.2 |  1.0$\pm$1.1 |  12.1$\pm$4.5 |  17.6$\pm$5.6 |  13.2$\pm$4.7
23.0 |  28899$\pm$2531 |  29775$\pm$2601 |  29204$\pm$2553 |  23801$\pm$2096 |  23398$\pm$2061 |  29.9$\pm$7.9 |  9.0$\pm$3.7 |  0.7$\pm$0.9 |  1.3$\pm$1.3 |  9.4$\pm$3.8 |  18.8$\pm$5.9 |  19.1$\pm$5.9
23.5 |  37206$\pm$3233 |  34960$\pm$3039 |  31488$\pm$2746 |  21688$\pm$1916 |  21586$\pm$1908 |  21.8$\pm$6.4 |  9.4$\pm$3.8 |  0.3$\pm$0.6 |  0.5$\pm$0.7 |  8.2$\pm$3.5 |  17.4$\pm$5.6 |  19.6$\pm$6.0
24.0 |  41654$\pm$3608 |  35913$\pm$3119 |  31040$\pm$2708 |  18234$\pm$1622 |  18162$\pm$1616 |  12.4$\pm$4.5 |  8.0$\pm$3.5 |  0.3$\pm$0.6 |  0.7$\pm$0.9 |  9.9$\pm$3.9 |  13.2$\pm$4.7 |  24.3$\pm$6.9
24.5 |  38743$\pm$3362 |  32089$\pm$2797 |  27997$\pm$2451 |  14033$\pm$1263 |  13840$\pm$1247 |  7.5$\pm$3.3 |  12.4$\pm$4.5 | - |  0.5$\pm$0.7 |  10.2$\pm$4.0 |  10.7$\pm$4.1 |  20.3$\pm$6.2
25.0 |  30352$\pm$2654 |  25586$\pm$2247 |  23053$\pm$2032 |  9904$\pm$907 |  9915$\pm$908 |  4.9$\pm$2.6 |  10.4$\pm$4.1 | - |  0.8$\pm$1.0 |  7.7$\pm$3.4 |  14.7$\pm$5.0 |  23.1$\pm$6.7
25.5 |  21584$\pm$1911 |  18657$\pm$1658 |  17589$\pm$1567 |  6704$\pm$629 |  6675$\pm$626 |  1.0$\pm$1.1 |  10.9$\pm$4.2 | - |  0.7$\pm$0.9 |  8.0$\pm$3.5 |  15.7$\pm$5.3 |  15.4$\pm$5.2
26.0 |  13944$\pm$1257 |  12613$\pm$1141 |  12319$\pm$1116 |  4359$\pm$422 |  4380$\pm$423 |  2.6$\pm$1.8 |  10.7$\pm$4.1 |  0.2$\pm$0.4 |  1.2$\pm$1.2 |  6.7$\pm$3.1 |  11.2$\pm$4.3 |  20.6$\pm$6.2
26.5 |  7989$\pm$742 |  8253$\pm$764 |  8058$\pm$747 |  2813$\pm$282 |  2762$\pm$278 |  0.9$\pm$1.0 |  12.4$\pm$4.5 | - |  0.2$\pm$0.4 |  6.9$\pm$3.2 |  12.7$\pm$4.6 |  17.7$\pm$5.7
27.0 |  4208$\pm$409 |  5398$\pm$514 |  5139$\pm$491 |  1774$\pm$187 |  1763$\pm$186 |  0.9$\pm$1.0 |  11.9$\pm$4.4 | - |  0.3$\pm$0.6 |  6.5$\pm$3.1 |  12.9$\pm$4.6 |  17.6$\pm$5.6
27.5 |  2219$\pm$228 |  3350$\pm$331 |  3201$\pm$318 |  1129$\pm$126 |  1112$\pm$124 |  0.9$\pm$1.0 |  8.7$\pm$3.7 | - |  1.2$\pm$1.2 |  7.5$\pm$3.4 |  12.2$\pm$4.5 |  13.9$\pm$4.9

Table 6: DEVILS photometric catalogue columns available in DEVILS_PhotomCat_15_10_2020_v0.5. The *B* values are available for FUV NUV ugrizYJHKs S36 S45 S58 S80 MIPS24 MIPS70 P100 P160 S250 S350 S500$\mu$m bands.  Column Name | Unit | UCD | Description
---|---|---|---
UID | none | meta.id | UID based on field location, and source RAmax and DECmax
NewSegID | none | meta.id | Segment ID derived from segmentation maps - unique over full field
field | none | meta.id | DEVILS Field identifier (D02/D03/D10)
RAcen | deg | pos.eq.ra | Right ascension of the ProFound segment centre in the VISTA Y-band image
DECcen | deg | pos.eq.dec | Declination of the ProFound segment centre in the VISTA Y-band image
RAmax | deg | pos.eq.ra | Right ascension of the brightest pixel in ProFound segment in the VISTA Y-band image
DECmax | deg | pos.eq.dec | Declinations of the brightest pixel in ProFound segment in the VISTA Y-band image
mask | none | meta.id | Mask flag to identify is segment falls in a masked region. 0=not masked, 1=masked
artefactFlag | none | meta.id | Artefact flag 0=Not artefact, ¿0=artefact
starFlag | none | meta.id | Star/galaxy classifier. 0=galaxy, 1=star
R50_Y | ′′ | phys.angSize;em.IR.Y | The segment radius contain 50% of the VISTA Y-band flux
N100_Y | none | phys.area | Number of segment pixels in the VISTA Y-band
subFrame | none | meta.id | ProFound processing subFrame within the field that the segment is taken from
segIDFrame | none | meta.id | Segment ID within subFrame
xcenFrame | pixel | instr.pixel | x position of the segment centre within the subFrame in the VISTA Y-band
ycenFrame | pixel | instr.pixel | y position of the segment centre within the subFrame in the VISTA Y-band
xmaxFrame | pixel | instr.pixel | x position of the segment brightest pixel within the subFrame in the VISTA Y-band
ymaxFrame | pixel | instr.pixel | y position of the segment brightest pixel within the subFrame in the VISTA Y-band
EBV | none | phys.excitParam | Galactic Extinction coefficient applied - derived from Planck maps
flux_*B* | Jy | phot.flux.density;em.*B* | Total flux measured in *B*
flux_err_*B* | Jy | stat.error;phot.flux.density;em.*B* | Error on total flux measured in *B*
flux_*B*_c | Jy | phot.flux.density;em.*B* | Colour-optimised flux measured in *B*
flux_err_*B*_c | Jy | stat.error;phot.flux.density;em.*B* | Error on colour-optimised flux measured in *B*

##  9 Summary

We have derived a new photometric catalogue for the DEVILS regions for use in
multiple scientific projects within the DEVILS team, which will then be
provided to the wider community. Photometry is derived from the up-to-date
imaging in these regions using the new ProFound source detection and
photometry code, which is designed to overcome a number of known issues
present in previous photometry. We perform a number of detailed checks and
manual fixing of the ProFound segmentation maps to improve the validity of our
photometric measurements and then drive new source photometry measurements
from the UV-FIR. We apply a masking, artefact flagging and star/galaxy
separation process to identify true extragalactic sources. We then undertake a
series of comparisons to existing photometry in the D10 (COSMOS) region to
highlight the validity of our catalogues and their use for scientific projects
within DEVILS and the wider community. We also use these tests to suggest that
our photometry is comparable or better than existing photometry in these
regions. We note that these updates to the photometry can have a strong impact
on derived galaxy evolution properties (such as the evolution of the sSFR-M*
relation). Finally, we use our catalogues to derive deep number counts in all
photometric bands analysed in this work, which are measured consistently from
the FUV to 500 $\mu$m, show consistency between individual fields and provide
state-of-the-art galaxy number counts to faint magnitudes for the astronomical
community.

## Acknowledgements

LJMD, ASGR and LC acknowledge support from the Australian Research Councils
Future Fellowship scheme (FT200100055, FT200100375 and FT180100066,
respectively). JET is supported by the Australian Government Research Training
Program (RTP) Scholarship. SB and SPD acknowledge support from the Australian
Research Councils Discovery Project scheme (DP180103740). MS has been
supported by the European Union’s Horizon 2020 Research and Innovation
programme under the Maria Sklodowska-Curie grant agreement (No. 754510), the
Polish National Science Centre (UMO-2016/23/N/ST9/02963), and the Spanish
Ministry of Science and Innovation through the Juan de la Cierva-formacion
programme (FJC2018-038792-I). MJJ acknowledges support from the UK Science and
Technology Facilities Council [ST/S000488/1 and ST/N000919/1], the Oxford
Hintze Centre for Astrophysical Surveys which is funded through generous
support from the Hintze Family Charitable Foundation and the South African
Radio Astronomy Observatory (SARAO), and ICRAR for financial support during a
sabbatical visit. MBi is supported by the Polish National Science Center
through grants no. 2020/38/E/ST9/00395, 2018/30/E/ST9/00698 and
2018/31/G/ST9/03388, and by the Polish Ministry of Science and Higher
Education through grant DIR/WK/2018/12. MV acknowledges support from the
Italian Ministry of Foreign Affairs and International Cooperation (MAECI Grant
Number ZA18GR02) and the South African Department of Science and Innovation’s
National Research Foundation (DSI-NRF Grant Number 113121) as part of the
ISARP RADIOSKY2020 Joint Research Scheme. NM acknowledges support from the
Bundesministerium für Bildung und Forschung (BMBF) award 05A20WM4. MBr
acknowledges the support of the University of Western Australia through a
Scholarship for International Research Fees and Ad Hoc Postgraduate
Scholarship. DEVILS is an Australian project based around a spectroscopic
campaign using the Anglo-Australian Telescope. DEVILS is part funded via
Discovery Programs by the Australian Research Council and the participating
institutions. The DEVILS website is https://devilsurvey.org. The DEVILS data
is hosted and provided by AAO Data Central (datacentral.org.au).

##  10 Data Availability

The photometry catalogues and associated meta data derived in this work are
currently made available to the DEVILS team for internal use via a Data
Management Unit (DMU) describing the data origin, and providing all
photometric measurements and other useful parameters provided by ProFound.
Each DMU is both version and date stamped for ease of use and to keep track of
updates. The catalogue used in this paper is DEVILS_PhotomCat_15_10_2020_v0.5
with columns described in Table 6. The data are currently available for
proprietary data projects within DEVILS. However, once these data have been
used for core DEVILS science projects, it will be made available to the wider
community via AAO data central101010https://datacentral.org.au/.

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