J/MNRAS/513/3719 Photometric Redshifts in COSMOS and XMM-LSS (Hatfield+, 2022)
Hybrid photometric redshifts for sources in the COSMOS and XMM-LSS fields.
Hatfield P.W., Jarvis M.J., Adams N., Bowler R.A.A., Haussler B.,
Duncan K.J.
<Mon. Not. R. Astron. Soc., 513, 3719-3733 (2022)>
=2022MNRAS.513.3719H 2022MNRAS.513.3719H (SIMBAD/NED BibCode)
ADC_Keywords: Surveys ; Photometry, infrared ; Photometry ; Optical ;
Redshifts ; Galaxies
Keywords: techniques: photometric - surveys - galaxies: distances and redshifts
Abstract:
In this paper, we present photometric redshifts for 2.7 million
galaxies in the XMM-LSS and COSMOS fields, both with rich optical and
near-infrared data from VISTA and HyperSuprimeCam. Both template
fitting (using galaxy and active galactic nuclei templates within
LePhare) and machine learning (using gpz) methods are run on the
aperture photometry of sources selected in the Ks-band. The resulting
predictions are then combined using a Hierarchical Bayesian model, to
produce consensus photometric redshift point estimates and probability
distribution functions that outperform each method individually. Our
point estimates have a root mean square error of ∼0.08-0.09, and
an outlier fraction of ∼3-4 percent when compared to spectroscopic
redshifts. We also compare our results to the COSMOS2020 photometric
redshifts, which contain fewer sources, but had access to a larger
number of bands and greater wavelength coverage, finding that
comparable photo-z quality can be achieved (for bright and
intermediate luminosity sources where a direct comparison can be
made). Our resulting redshifts represent the most accurate set of
photometric redshifts (for a catalogue this large) for these deep
multisquare degree multiwavelength fields to date.
Description:
A catalogue of photometric redshift estimates for 2.7 million sources
in the COSMOS and XMM-LSS extragalactic fields. Both optical and
near-infrared photomet ry is used for the calculation of the estimates
(u, g, r, i, z, y, Y, J, H, Ks). Three sets of photometric redshift
estimates (alongside the photometry) are included in the catalogue: i)
calculated using template fitting, ii) calculated using machine
learning, and iii) a hybrid method that seeks to combine i) and ii).
COSMOS and XMM-LSS are two well studied deep extragalactic fields with
a rich range of multiwavelength data. Here we present calculations of
photometric reds hifts in the fields based on deep optical and
near-infrared data. Identical procedures were used to extract the
photometry across all the data - the data is thus very homogenous
across the two fields.
The photometry used spans 10 filters; u (Canada-France-Hawaii
telescope Large Area U-band Deep Survey, CLAUDS, for both COSMOS and
the wider area XMM-LSS), grizy (Hyper Suprime-Cam Subaru Strategic
Program, HSC-SSP, for both COSMOS and XMM-LSS) and YJHKs (Visible and
Infrared Survey Telescope for Astronomy, VI STA, VISTA Deep
Extragalactic Observations, VIDEO for XMM-LSS, and UltraVISTA for
COSMOS). The photometric depths in the COSMOS (XMM-LSS) field are
u=27.0 (=26.9), g=27.2 (=27.0), r=26.8 (=26.5), i=26.6 (=26.4), z=25.9
(=26.3), y_HSC=25.5 (=25.6), Y_VISTA=25.5 (=25.2), J=25.3 (=24.7),
H=25.0 (=24.3) and Ks=24.8 (=23.9). Depths are not quite identical for
the three VISTA tiles in XMM-LSS; here the deepest data value is
quoted.
Photometric redshifts are calculated in three ways:
i) Using Template-fitting with LePhare (as per Adams et al.,
2020MNRAS.494.1771A 2020MNRAS.494.1771A)
ii) Using machine learning with GPz (as per Hatfield et al.,
2020MNRAS.498.5498H 2020MNRAS.498.5498H)
iii) Using a Hierarchical Bayesian model to combine i) and ii) (as per
Duncan et al., 2018MNRAS.477.5177D 2018MNRAS.477.5177D)
The relative merits of the three methods are discussed and evaluated
in Hatfield et al. (2020MNRAS.498.5498H 2020MNRAS.498.5498H), but in general iii) is found
to perform best and take the best aspects of approaches i) and ii).
File Summary:
--------------------------------------------------------------------------------
FileName Lrecl Records Explanations
--------------------------------------------------------------------------------
ReadMe 80 . This file
cosphotz.dat 619 995049 Data in the COSMOS field
xmmphotz.dat 633 1674689 Data in the XMM-LSS field
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See also:
http://www-astro.physics.ox.ac.uk/~video/public/Home.html : VIDEO Home Page
https://ultravista.org/ : UltraVISTA Home Page
https://hsc-release.mtk.nao.ac.jp/doc/ : HSC data Home Page
https://www.ap.smu.ca/~sawicki/sawicki/CLAUDS.html : CLAUDS Home Page
Byte-by-byte Description of file: cosphotz.dat
--------------------------------------------------------------------------------
Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
1- 18 F18.14 deg RAdeg Celestial right ascension (J2000) (RA2000)
20- 37 F18.16 deg DEdeg Celestial declination (J2000) (DEC2000)
39- 48 F10.8 --- zphMLbest Machine learning (GPz) based photometric
redshift estimate (photozML_best)
50- 57 F8.6 --- e_zphMLbest Uncertainty on zphMLbest (photozML_err)
59- 78 F20.18 --- zphTemp Template fitting (LePhare) based
photometric redshift estimate
(photozTEMPLATE_best)
80- 98 F19.17 --- e_zphTemp Uncertainty on zphTemp
(photozTEMPLATE_err)
100-120 F21.19 --- zphHBbest Hybrid (Hierarchical Bayesian combination)
photometric redshift estimate
(photozHB_best)
122-141 E20.18 --- e_zphHBbest ? Uncertainty on zphHBbest
(photozHB_err)
143 I1 --- Flag [0/1] Flag identifying quality of
photo-z (flag) (G1)
145-167 E23.17 mW/m2/Hz FCFHT-u ?=-99 u-band flux (from CFHT data)
(flux_CFHT-u)
169-191 E23.17 mW/m2/Hz FHSC-g ?=-99 g-band flux (from HSC data)
(flux_HSC-G)
193-215 E23.17 mW/m2/Hz FHSC-r ?=-99 r-band flux (from HSC data)
(flux_HSC-R)
217-239 E23.17 mW/m2/Hz FHSC-i ?=-99 i-band flux (from HSC data)
(flux_HSC-I)
241-263 E23.17 mW/m2/Hz FHSC-z ?=-99 z-band flux (from HSC data)
(flux_HSC-Z)
265-287 E23.17 mW/m2/Hz FHSC-y ?=-99 y-band flux (from HSC data)
(flux_HSC-Y)
289-311 E23.17 mW/m2/Hz FVISTA-Y ?=-99 Y-band flux (from VISTA data)
(fluxVISTAY)
313-335 E23.17 mW/m2/Hz FVISTA-J ?=-99 J-band flux (from VISTA data)
(fluxVISTAJ)
337-359 E23.17 mW/m2/Hz FVISTA-H ?=-99 H-band flux (from VISTA data)
(fluxVISTAH)
361-383 E23.17 mW/m2/Hz FVISTA-Ks ?=-99 Ks-band flux (from VISTA data)
(fluxVISTAKs)
385-407 E23.17 mW/m2/Hz e_FCFHT-u []?=-99 Uncertainty on u-band flux
(from CFHT data) (err_CFHT-u)
409-431 E23.17 mW/m2/Hz e_FHSC-g ?=-99 Uncertainty on g-band flux
(from HSC data) (err_HSC-G)
433-455 E23.17 mW/m2/Hz e_FHSC-r ?=-99 Uncertainty on r-band flux
(from HSC data) (err_HSC-R)
457-479 E23.17 mW/m2/Hz e_FHSC-i ?=-99 Uncertainty on i-band flux
(from HSC data) (err_HSC-I)
481-503 E23.17 mW/m2/Hz e_FHSC-z ?=-99 Uncertainty on z-band flux
(from HSC data) (err_HSC-Z)
505-527 E23.17 mW/m2/Hz e_FHSC-y ?=-99 Uncertainty on y-band flux
(from HSC data) (err_HSC-Y)
529-550 E22.17 mW/m2/Hz e_FVISTA-Y Uncertainty on Y-band flux
(from VISTA data) (errVISTAY)
552-573 E22.17 mW/m2/Hz e_FVISTA-J Uncertainty on J-band flux
(from VISTA data) (errVISTAJ)
575-596 E22.17 mW/m2/Hz e_FVISTA-H Uncertainty on H-band flux
(from VISTA data) (errVISTAH)
598-619 E22.17 mW/m2/Hz e_FVISTA-Ks ? Uncertainty on Ks-band flux
(from VISTA data) (errVISTAKs)
--------------------------------------------------------------------------------
Byte-by-byte Description of file: xmmphotz.dat
--------------------------------------------------------------------------------
Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
1- 18 F18.15 deg RAdeg Celestial right ascension (J2000) (RA2000)
20- 38 F19.16 deg DEdeg Celestial declination (J2000) (DEC2000)
40- 49 E10.5 --- zphMLbest Machine learning (GPz) based photometric
redshift estimate (photozML_b)
51- 58 F8.6 --- e_zphMLbest Uncertainty on zphMLbest (photozML_e)
60- 80 F21.19 --- zphTemp Template fitting (LePhare) based
photometric redshift estimate
(photozTEMP)
82-100 F19.17 --- e_zphTemp Uncertainty on zphTemp (photozTEMP)
102-122 F21.19 --- zphHBbest Hybrid (Hierarchical Bayesian combination)
photometric redshift estimate
(photozHB_b)
124-145 F22.18 --- e_zphHBbest ? Uncertainty on zphHBbest (photozHB_e)
147 I1 --- Flag [0/1] Flag identifying quality of photo-z
(flag) (G1)
149-172 E24.1 mW/m2/Hz FCFHT-u ?=-99 u-band flux (from CFHT data)
(flux_CFHT-u)
174-197 E24.17 mW/m2/Hz FHSC-g ?=-99 g-band flux (from HSC data)
(flux_HSC-G)
199-222 E24.17 mW/m2/Hz FHSC-r ?=-99 r-band flux (from HSC data)
(flux_HSC-R)
224-247 E24.17 mW/m2/Hz FHSC-i ?=-99 i-band flux (from HSC data)
(flux_HSC-I)
249-272 E24.17 mW/m2/Hz FHSC-z ?=-99 z-band flux (from HSC data)
(flux_HSC-Z)
274-297 E24.17 mW/m2/Hz FHSC-y ?=-99 y-band flux (from HSC data)
(flux_HSC-Y)
299-322 E24.17 mW/m2/Hz FVISTA-Y ?=-99 Y-band flux (from VISTA data)
(fluxVISTAY)
324-347 E24.17 mW/m2/Hz FVISTA-J ?=-99 J-band flux (from VISTA data)
(fluxVISTAJ)
349-372 E24.17 mW/m2/Hz FVISTA-H ?=-99 H-band flux (from VISTA data)
(fluxVISTAH)
374-397 E24.17 mW/m2/Hz FVISTA-Ks ?=-99 Ks-band flux (from VISTA data)
(fluxVISTAK)
399-421 E23.17 mW/m2/Hz e_FCFHT-u ?=-99 Uncertainty on u-band flux
(from CFHT data) (err_CFHT-u)
423-445 E23.17 mW/m2/Hz e_FHSC-g ?=-99 Uncertainty on g-band flux
(from HSC data) (err_HSC-G)
447-469 E23.17 mW/m2/Hz e_FHSC-r ?=-99 Uncertainty on r-band flux
(from HSC data) (err_HSC-R)
471-493 E23.17 mW/m2/Hz e_FHSC-i ?=-99 Uncertainty on i-band flux
(from HSC data) (err_HSC-I)
495-517 E23.17 mW/m2/Hz e_FHSC-z ?=-99 Uncertainty on z-band flux
(from HSC data) (err_HSC-Z)
519-541 E23.17 mW/m2/Hz e_FHSC-y ?=-99 Uncertainty on y-band flux
(from HSC data) (err_HSC-Y)
543-564 E22.17 mW/m2/Hz e_FVISTA-Y ?=-99 Uncertainty on Y-band flux
(from VISTA data) (errVISTAY)
566-587 E22.17 mW/m2/Hz e_FVISTA-J ?=-99 Uncertainty on J-band flux
(from VISTA data) (errVISTAJ)
589-610 E22.17 mW/m2/Hz e_FVISTA-H ?=-99 Uncertainty on H-band flux
(from VISTA data) (errVISTAH)
612-633 E22.17 mW/m2/Hz e_FVISTA-Ks ?=-99 Uncertainty on Ks-band flux
(from VISTA data) (errVISTAKs)
--------------------------------------------------------------------------------
Global notes:
Note (G1): Quality flag as follows:
1 = reliable
0 = potentially unreliable (source in colour-magnitude stellar locus,
or chi2star<min(chi2QSO,chi2_gal)
--------------------------------------------------------------------------------
History:
From Peter Hatfield, astropeterhatfield(at)gmail.com
Acknowledgements:
Please include the following acknowledgment in any published material
that makes use of this data products:
Based on data products from observations made with ESO Telescopes at
the La Silla or Paranal Observatories under ESO programme ID 179.A-
2006. Based on observations obtained with MegaPrime/MegaCam, a joint
project of CFHT and CEA/IRFU, at the Canada-France-Hawaii Telescope
(CFHT) which is operated by the National Research Council (NRC) of
Canada, the Institut National des Science de l'Univers of the Centre
National de la Recherche Scientifique (CNRS) of France, and the
University of Hawaii. This work is based in part on data products
produced at Terapix available at the Canadian Astronomy Data Centre as
part of the Canada-France-Hawaii Telescope Legacy Survey, a
collaborative project of NRC and CNRS.
This paper is based, in part, on data collected at the Subaru
Telescope and retrieved from the Hyper Suprime-Cam (HSC) data archive
system, which is operated by Subaru Telescope and Astronomy Data
Center at National Astronomical Observatory of Japan. Data analysis
was in part carried out with the cooperation of Center for
Computational Astrophysics, National Astronomical Observatory of
Japan. The HSC collaboration includes the astronomical communities of
Japan and Taiwan, and Princeton University. The HSC instrumentation
and software were developed by the National Astronomical Observatory
of Japan (NAOJ), the Kavli Institute for the Physics and Mathematics
of the Universe (Kavli IPMU), the University of Tokyo, the High Energy
Accelerator Research Organization (KEK), the Academia Sinica Institute
for Astronomy and Astrophysics in Taiwan (ASIAA), and Princeton
University. Funding was contributed by the FIRST program from Japanese
Cabinet Office, the Ministry of Education, Culture, Sports, Science
and Technology (MEXT), the Japan Society for the Promotion of Science
(JSPS), Japan Science and Technology Agency (JST), the Toray Science
Foundation, NAOJ, Kavli IPMU, KEK, ASIAA, and Princeton University.
References:
Adams et al., 2020MNRAS.494.1771A 2020MNRAS.494.1771A
Duncan et al., 2018MNRAS.477.5177D 2018MNRAS.477.5177D
Hatfield et al., 2020MNRAS.498.5498H 2020MNRAS.498.5498H
(End) Peter Hatfield [University of Oxford], Patricia Vannier [CDS] 01-Aug-2022