J/A+A/703/A261 TOPz photo-z for the GAMA sample (Tempel+, 2025)
TOPz: photometric redshifts using template fitting applied to GAMA survey.
Tempel E., Laur J., Jones Z.R., Kipper R., Liivamaegi L.J., Pandey D.,
Sakteos G., Tamm A., Triantafyllaki A.N., Tuvikene T.
<Astron. Astrophys. 703, A261 (2025)>
=2025A&A...703A.261T 2025A&A...703A.261T (SIMBAD/NED BibCode)
ADC_Keywords: Galaxy catalogs ; Redshifts ; Photometry ; Optical
Keywords: methods: observational - methods: statistical -
techniques: photometric - catalogs -
galaxies: distances and redshifts - galaxies: general
Abstract:
Accurate photometric redshift (photo-z) estimation is crucial for
cosmological and galaxy evolution studies, especially with the advent
of large-scale photometric surveys.
We develop a photo-z estimation code called TOPz (Tartu Observatory
Photo-z) and apply it to the GAMA photometric catalogue. TOPz employs
a Bayesian template-fitting approach to estimate photo-z from
marginalised redshift posteriors. Using nine-band photometric data
from the GAMA project, we assess the accuracy of TOPz by comparing its
photo-z estimates to available spectroscopic redshifts from GAMA and
DESI DR1. The latter extends to z < 2 and mZ < 24, allowing the
photo-z accuracy to be validated beyond the GAMA limits.
TOPz employs a Bayesian template-fitting approach to estimate photo-z
from marginalised redshift posteriors. We generated synthetic galaxy
spectra using the CIGALE software and ran template set optimisation.
We improved the photometry by applying flux and flux uncertainty
corrections. An analytical prior was then imposed on the resulting
posteriors to refine the redshift estimates.
The photo-z estimates produced by TOPz show good agreement with the
spectroscopic redshifts in the low-redshift regime (z<0.5) where the
majority (95%) of the GAMA spectroscopic redshifts are. We demonstrate
the redshift accuracy across various magnitude bins and tested how the
flux corrections and posteriors reflect the actual uncertainty of the
estimates. For the GAMA sample, the σNMAD=0.012 for mZ<18 and
increases to σNMAD=0.021 for mZ>19. The outlier fraction
(|Δz|/(1+z)>0.1) in the same magnitude bins increases from 1 to
5%. We show that the TOPz results are consistent with those obtained
from other photo-z codes (EAZY and SFM) applied to the same data set.
Additionally, TOPz estimates stellar masses as a by-product,
comparable to those calculated by other methods. We have made the full
GAMA photo-z catalogue and all the codes and scripts used for the
analysis and figures publicly available.
TOPz is an advanced photo-z estimation code that integrates flux
corrections, physical priors, and template set optimisation to provide
state-of-the-art photo-z among competing template-based redshift
estimators. Future work will focus on incorporating additional
photometric data and applying the TOPz algorithm to the J-PAS
narrow-band survey, further validating and enhancing its capabilities.
Description:
We present the TOPz photo-z catalogue for the GAMA photometric sample.
The photometric input data are taken from the GAMA project DR4
gkvScienceCatv02 catalogue, which is a "science-ready" subset of the
entire GAMA input catalogue (Bellstedt et al. 2020MNRAS.496.3235B 2020MNRAS.496.3235B).
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
topzgama.dat 318 2232985 TOPz photo-z catalogue
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Byte-by-byte Description of file: topzgama.dat
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Bytes Format Units Label Explanations
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1- 15 I15 --- uberID Unique GAMA III ID of object (uberID)
17- 23 I7 --- CataID ?=0 ID of best-matching GAMA II object
(CATAID)
25- 32 F8.5 --- zsp ?=-9.999 Spectroscopic redshift from the
GAMA database (zspec)
34- 43 F10.6 deg RAdeg Right ascension of maximum flux,
corrected for Gaia offset (ICRS) (ra)
45- 54 F10.6 deg DEdeg Declination of maximum flux,
corrected for Gaia offset (ICRS) (dec)
56- 64 F9.6 mag mag Magnitude in the r + Z detection band (mag)
66 I1 --- Nflux Number of used fluxes for photo-z estimation
(nflux)
68- 79 E12.6 --- zetaw Posterior-weighted redshift (zeta = ln(1 + z))
(zeta_w)
81- 92 E12.6 Msun Massw Posterior-weighted stellar mass estimate
(mass_w)
94-105 E12.6 --- zetabest Posterior-weighted redshift from the best peak
(zeta_best)
107-118 E12.6 --- zetabestp Summed probability in the best peak
(zetabestp)
120-131 E12.6 --- b_zetabest Minimum best-peak redshift (zetabestmin)
133-144 E12.6 --- B_zetabest Maximum best-peak redshift (zetabestmax)
146-157 E12.6 --- s_zetabest Weighted standard deviation of the best-peak
redshift (zetabestsigma)
159-170 E12.6 --- zetaAlt1 Posterior-weighted redshift from the
first alternative peak (zeta_alt1)
172-183 E12.6 --- zetaAlt1p Summed probability in the
first alternative peak (zetaalt1p)
185-196 E12.6 --- b_zetaAlt1 Minimum first-alternative-peak redshift
(zetaalt1min)
198-209 E12.6 --- B_zetaAlt1 Maximum first-alternative-peak redshift
(zetaalt1max)
211-222 E12.6 --- s_zetaAlt1 Weighted standard deviation of the
first-alternative-peak redshift
(zetaalt1sigma)
224-235 E12.6 --- zetaAlt2 Posterior-weighted redshift from the
second alternative peak (zeta_alt2)
237-248 E12.6 --- zetaAlt2p Summed probability in the
second alternative peak (zetaalt2p)
250-261 E12.6 --- b_zetaAlt2 Minimum second-alternative-peak redshift
(zetaalt2min)
263-274 E12.6 --- B_zetaAlt2 Maximum second-alternative-peak redshift
(zetaalt2max)
276-287 E12.6 --- s_zetaAlt2 Weighted standard deviation of the
second-alternative-peak redshift
(zetaalt2sigma)
289-290 I2 --- Npeak Number of identified peaks in the photo-z
posterior (npeak)
292-303 E12.6 --- chi2mean Posterior-weighted mean chi-squared value
(chi2_mean)
305-317 E13.6 --- IsStar TOPz probability that this object is a star
(is_star)
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Acknowledgements:
Elmo Tempel, elmo.tempel(at)ut.ee,
Taavi Tuvikene, taavi.tuvikene(at)ut.ee
(End) Patricia Vannier [CDS] 29-Oct-2025