Photometric redshift estimation for Rubin Observatory Data Preview 1 with Redshift Assessment Infrastructure Layers

T. Zhang; E. Charles; J. F. Crenshaw; S. J. Schmidt; P. Adari; J. Gschwend; S. Mau; B. H. Andrews; E. Aubourg; Y. Bains; K. Bechtol; A. Boucaud; D. Boutigny; P. Burchat; J. Chevalier; J. Chiang; H. F. Chiang; D. Clowe; J. Cohen-Tanugi; C. Combet; A. Connolly; S. Dagoret-Campagne; P. N. Daly; F. Daruich; G. Daubard; J. De Vicente; H. Drass; K. Fanning; E. Gawiser; M. Graham; L. P. Guy; Q. Hang; P. Ingraham; O. Ilbert; M. Jarvis; M. J. Jee; T. Jenness; A. Johnson; S. Joudaki; C. Juramy-Gilles; S. M. Kahn; J. B. Kalmbach; Y. Kang; A. Kannawadi; L. S. Kelvin; S. Liang; O. Lynn; N. B. Lust; M. Lutfi; A. Malz; R. Mandelbaum; S. Marshall; J. Meyers; M. Migliore; M. Moniez; I. Moskowitz; J. Neveu; J. A. Newman; E. Nourbakhsh; D. Oldag; H. Park; S. Pelesky; A. A.Plazas Malagón; B. Quint; M. Rahman; A. Rasmussen; K. Reil; M. Ricci; W. Roby; A. Roodman; C. Roucelle; M. Salvato; B. Sánchez; D. Sanmartim; R. H. Schindler; J. Scora; J. Sebag; N. Sedaghat; I. Sevilla-Noarbe; R. Shirley; A. Shugart; R. Solomon; D. S. Taranu; G. Thayer; L. Toribio San Cipriano; E. Urbach; Y. Utsumi; W. van Reeven; A. von der Linden; C. W. Walter; W. M. Wood-Vasey; Z. Zhang; J. Zuntz

doi:10.1093/mnras/stag1012

Photometric redshift estimation for Rubin Observatory Data Preview 1 with Redshift Assessment Infrastructure Layers

T. Zhang^*
, E. Charles
, J. F. Crenshaw
, S. J. Schmidt
, P. Adari
, J. Gschwend
, S. Mau
, B. H. Andrews
, E. Aubourg
, Y. Bains
, K. Bechtol
, A. Boucaud
, D. Boutigny
, P. Burchat
, J. Chevalier
, J. Chiang
, H. F. Chiang
, D. Clowe
, J. Cohen-Tanugi
, C. Combet

A. Connolly, S. Dagoret-Campagne, P. N. Daly, F. Daruich, G. Daubard, J. De Vicente, H. Drass, K. Fanning, E. Gawiser, M. Graham, L. P. Guy, Q. Hang, P. Ingraham, O. Ilbert, M. Jarvis, M. J. Jee, T. Jenness, A. Johnson, S. Joudaki, C. Juramy-Gilles, S. M. Kahn, J. B. Kalmbach, Y. Kang, A. Kannawadi, L. S. Kelvin, S. Liang, O. Lynn, N. B. Lust, M. Lutfi, A. Malz, R. Mandelbaum, S. Marshall, J. Meyers, M. Migliore, M. Moniez, I. Moskowitz, J. Neveu, J. A. Newman, E. Nourbakhsh, D. Oldag, H. Park, S. Pelesky, A. A.Plazas Malagón, B. Quint, M. Rahman, A. Rasmussen, K. Reil, M. Ricci, W. Roby, A. Roodman, C. Roucelle, M. Salvato, B. Sánchez, D. Sanmartim, R. H. Schindler, J. Scora, J. Sebag, N. Sedaghat, I. Sevilla-Noarbe, R. Shirley, A. Shugart, R. Solomon, D. S. Taranu, G. Thayer, L. Toribio San Cipriano, E. Urbach, Y. Utsumi, W. van Reeven, A. von der Linden, C. W. Walter, W. M. Wood-Vasey, Z. Zhang, J. Zuntz

^*Corresponding author for this work

Institute of Cosmology & Gravitation

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Abstract

We present the first systematic analysis of photometric redshifts (photo-z) estimated from the Rubin Observatory Data Preview 1 (DP1) data taken with the Legacy Survey of Space and Time (LSST) Commissioning Camera. Employing the Redshift Assessment Infrastructure Layers (RAIL) framework, we apply eight photo-z algorithms to the DP1 photometry, using deep (Formula presented) coverage in the Extended Chandra Deep Field South (ECDFS) field and (Formula presented) data in the Rubin_SV_38_7 field. In the ECDFS field, we construct a reference catalogue from spectroscopic redshift (spec-z), grism redshift (grism-z), and multiband photo-z for training and validating photo-z. Performance metrics of the photo-z are evaluated using spec-zs from ECDFS and Dark Energy Spectroscopic Instrument Data Release 1 samples. Across the algorithms, we achieve per-galaxy photo-z scatter of (Formula presented) and outlier fractions around 10 per cent in the 6-band data, with performance degrading at faint magnitudes and (Formula presented). The overall bias and scatter of our machine-learning based photo-zs satisfy the LSST Y1 requirement. We also use our photo-z to infer the ensemble redshift distribution (Formula presented). We study the photo-z improvement by including near-infrared photometry from the Euclid mission, and find that Euclid photometry improves photo-z at (Formula presented). Our results validate the RAIL pipeline for Rubin photo-z production and demonstrate promising initial performance.

Original language	English
Article number	stag1012
Number of pages	17
Journal	Monthly Notices of the Royal Astronomical Society
Volume	550
Issue number	2
Early online date	3 Jul 2026
DOIs	https://doi.org/10.1093/mnras/stag1012
Publication status	Published - 1 Aug 2026

Keywords

galaxies: distances and redshifts
methods: statistical

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10.1093/mnras/stag1012

Photometric redshift estimation for Rubin Observatory Data Preview 1 with Redshift Assessment Infrastructure LayersFinal published version, 3.42 MBLicence: CC BY

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Zhang, T., Charles, E., Crenshaw, J. F., Schmidt, S. J., Adari, P., Gschwend, J., Mau, S., Andrews, B. H., Aubourg, E., Bains, Y., Bechtol, K., Boucaud, A., Boutigny, D., Burchat, P., Chevalier, J., Chiang, J., Chiang, H. F., Clowe, D., Cohen-Tanugi, J., ... Zuntz, J. (2026). Photometric redshift estimation for Rubin Observatory Data Preview 1 with Redshift Assessment Infrastructure Layers. Monthly Notices of the Royal Astronomical Society, 550(2), Article stag1012. https://doi.org/10.1093/mnras/stag1012

@article{27ca6857dafe4dc991da3ec84a76257f,

title = "Photometric redshift estimation for Rubin Observatory Data Preview 1 with Redshift Assessment Infrastructure Layers",

abstract = "We present the first systematic analysis of photometric redshifts (photo-z) estimated from the Rubin Observatory Data Preview 1 (DP1) data taken with the Legacy Survey of Space and Time (LSST) Commissioning Camera. Employing the Redshift Assessment Infrastructure Layers (RAIL) framework, we apply eight photo-z algorithms to the DP1 photometry, using deep (Formula presented) coverage in the Extended Chandra Deep Field South (ECDFS) field and (Formula presented) data in the Rubin\_SV\_38\_7 field. In the ECDFS field, we construct a reference catalogue from spectroscopic redshift (spec-z), grism redshift (grism-z), and multiband photo-z for training and validating photo-z. Performance metrics of the photo-z are evaluated using spec-zs from ECDFS and Dark Energy Spectroscopic Instrument Data Release 1 samples. Across the algorithms, we achieve per-galaxy photo-z scatter of (Formula presented) and outlier fractions around 10 per cent in the 6-band data, with performance degrading at faint magnitudes and (Formula presented). The overall bias and scatter of our machine-learning based photo-zs satisfy the LSST Y1 requirement. We also use our photo-z to infer the ensemble redshift distribution (Formula presented). We study the photo-z improvement by including near-infrared photometry from the Euclid mission, and find that Euclid photometry improves photo-z at (Formula presented). Our results validate the RAIL pipeline for Rubin photo-z production and demonstrate promising initial performance.",

keywords = "galaxies: distances and redshifts, methods: statistical",

author = "T. Zhang and E. Charles and Crenshaw, \{J. F.\} and Schmidt, \{S. J.\} and P. Adari and J. Gschwend and S. Mau and Andrews, \{B. H.\} and E. Aubourg and Y. Bains and K. Bechtol and A. Boucaud and D. Boutigny and P. Burchat and J. Chevalier and J. Chiang and Chiang, \{H. F.\} and D. Clowe and J. Cohen-Tanugi and C. Combet and A. Connolly and S. Dagoret-Campagne and Daly, \{P. N.\} and F. Daruich and G. Daubard and J. De Vicente and H. Drass and K. Fanning and E. Gawiser and M. Graham and Guy, \{L. P.\} and Q. Hang and P. Ingraham and O. Ilbert and M. Jarvis and Jee, \{M. J.\} and T. Jenness and A. Johnson and S. Joudaki and C. Juramy-Gilles and Kahn, \{S. M.\} and Kalmbach, \{J. B.\} and Y. Kang and A. Kannawadi and Kelvin, \{L. S.\} and S. Liang and O. Lynn and Lust, \{N. B.\} and M. Lutfi and A. Malz and R. Mandelbaum and S. Marshall and J. Meyers and M. Migliore and M. Moniez and I. Moskowitz and J. Neveu and Newman, \{J. A.\} and E. Nourbakhsh and D. Oldag and H. Park and S. Pelesky and Malag{\'o}n, \{A. A.Plazas\} and B. Quint and M. Rahman and A. Rasmussen and K. Reil and M. Ricci and W. Roby and A. Roodman and C. Roucelle and M. Salvato and B. S{\'a}nchez and D. Sanmartim and Schindler, \{R. H.\} and J. Scora and J. Sebag and N. Sedaghat and I. Sevilla-Noarbe and R. Shirley and A. Shugart and R. Solomon and Taranu, \{D. S.\} and G. Thayer and Cipriano, \{L. Toribio San\} and E. Urbach and Y. Utsumi and W. van Reeven and \{von der Linden\}, A. and Walter, \{C. W.\} and Wood-Vasey, \{W. M.\} and Z. Zhang and J. Zuntz",

note = "Publisher Copyright: {\textcopyright} The Author(s) 2026. Published by Oxford University Press on behalf of Royal Astronomical Society. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.",

year = "2026",

month = aug,

day = "1",

doi = "10.1093/mnras/stag1012",

language = "English",

volume = "550",

journal = "Monthly Notices of the Royal Astronomical Society",

issn = "0035-8711",

publisher = "Oxford University Press",

number = "2",

}

Zhang, T, Charles, E, Crenshaw, JF, Schmidt, SJ, Adari, P, Gschwend, J, Mau, S, Andrews, BH, Aubourg, E, Bains, Y, Bechtol, K, Boucaud, A, Boutigny, D, Burchat, P, Chevalier, J, Chiang, J, Chiang, HF, Clowe, D, Cohen-Tanugi, J, Combet, C, Connolly, A, Dagoret-Campagne, S, Daly, PN, Daruich, F, Daubard, G, De Vicente, J, Drass, H, Fanning, K, Gawiser, E, Graham, M, Guy, LP, Hang, Q, Ingraham, P, Ilbert, O, Jarvis, M, Jee, MJ, Jenness, T, Johnson, A, Joudaki, S, Juramy-Gilles, C, Kahn, SM, Kalmbach, JB, Kang, Y, Kannawadi, A, Kelvin, LS, Liang, S, Lynn, O, Lust, NB, Lutfi, M, Malz, A, Mandelbaum, R, Marshall, S, Meyers, J, Migliore, M, Moniez, M, Moskowitz, I, Neveu, J, Newman, JA, Nourbakhsh, E, Oldag, D, Park, H, Pelesky, S, Malagón, AAP, Quint, B, Rahman, M, Rasmussen, A, Reil, K, Ricci, M, Roby, W, Roodman, A, Roucelle, C, Salvato, M, Sánchez, B, Sanmartim, D, Schindler, RH, Scora, J, Sebag, J, Sedaghat, N, Sevilla-Noarbe, I, Shirley, R, Shugart, A, Solomon, R, Taranu, DS, Thayer, G, Cipriano, LTS, Urbach, E, Utsumi, Y, van Reeven, W, von der Linden, A, Walter, CW, Wood-Vasey, WM, Zhang, Z & Zuntz, J 2026, 'Photometric redshift estimation for Rubin Observatory Data Preview 1 with Redshift Assessment Infrastructure Layers', Monthly Notices of the Royal Astronomical Society, vol. 550, no. 2, stag1012. https://doi.org/10.1093/mnras/stag1012

TY - JOUR

T1 - Photometric redshift estimation for Rubin Observatory Data Preview 1 with Redshift Assessment Infrastructure Layers

AU - Zhang, T.

AU - Charles, E.

AU - Crenshaw, J. F.

AU - Schmidt, S. J.

AU - Adari, P.

AU - Gschwend, J.

AU - Mau, S.

AU - Andrews, B. H.

AU - Aubourg, E.

AU - Bains, Y.

AU - Bechtol, K.

AU - Boucaud, A.

AU - Boutigny, D.

AU - Burchat, P.

AU - Chevalier, J.

AU - Chiang, J.

AU - Chiang, H. F.

AU - Clowe, D.

AU - Cohen-Tanugi, J.

AU - Combet, C.

AU - Connolly, A.

AU - Dagoret-Campagne, S.

AU - Daly, P. N.

AU - Daruich, F.

AU - Daubard, G.

AU - De Vicente, J.

AU - Drass, H.

AU - Fanning, K.

AU - Gawiser, E.

AU - Graham, M.

AU - Guy, L. P.

AU - Hang, Q.

AU - Ingraham, P.

AU - Ilbert, O.

AU - Jarvis, M.

AU - Jee, M. J.

AU - Jenness, T.

AU - Johnson, A.

AU - Joudaki, S.

AU - Juramy-Gilles, C.

AU - Kahn, S. M.

AU - Kalmbach, J. B.

AU - Kang, Y.

AU - Kannawadi, A.

AU - Kelvin, L. S.

AU - Liang, S.

AU - Lynn, O.

AU - Lust, N. B.

AU - Lutfi, M.

AU - Malz, A.

AU - Mandelbaum, R.

AU - Marshall, S.

AU - Meyers, J.

AU - Migliore, M.

AU - Moniez, M.

AU - Moskowitz, I.

AU - Neveu, J.

AU - Newman, J. A.

AU - Nourbakhsh, E.

AU - Oldag, D.

AU - Park, H.

AU - Pelesky, S.

AU - Malagón, A. A.Plazas

AU - Quint, B.

AU - Rahman, M.

AU - Rasmussen, A.

AU - Reil, K.

AU - Ricci, M.

AU - Roby, W.

AU - Roodman, A.

AU - Roucelle, C.

AU - Salvato, M.

AU - Sánchez, B.

AU - Sanmartim, D.

AU - Schindler, R. H.

AU - Scora, J.

AU - Sebag, J.

AU - Sedaghat, N.

AU - Sevilla-Noarbe, I.

AU - Shirley, R.

AU - Shugart, A.

AU - Solomon, R.

AU - Taranu, D. S.

AU - Thayer, G.

AU - Cipriano, L. Toribio San

AU - Urbach, E.

AU - Utsumi, Y.

AU - van Reeven, W.

AU - von der Linden, A.

AU - Walter, C. W.

AU - Wood-Vasey, W. M.

AU - Zhang, Z.

AU - Zuntz, J.

N1 - Publisher Copyright: © The Author(s) 2026. Published by Oxford University Press on behalf of Royal Astronomical Society. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

PY - 2026/8/1

Y1 - 2026/8/1

N2 - We present the first systematic analysis of photometric redshifts (photo-z) estimated from the Rubin Observatory Data Preview 1 (DP1) data taken with the Legacy Survey of Space and Time (LSST) Commissioning Camera. Employing the Redshift Assessment Infrastructure Layers (RAIL) framework, we apply eight photo-z algorithms to the DP1 photometry, using deep (Formula presented) coverage in the Extended Chandra Deep Field South (ECDFS) field and (Formula presented) data in the Rubin_SV_38_7 field. In the ECDFS field, we construct a reference catalogue from spectroscopic redshift (spec-z), grism redshift (grism-z), and multiband photo-z for training and validating photo-z. Performance metrics of the photo-z are evaluated using spec-zs from ECDFS and Dark Energy Spectroscopic Instrument Data Release 1 samples. Across the algorithms, we achieve per-galaxy photo-z scatter of (Formula presented) and outlier fractions around 10 per cent in the 6-band data, with performance degrading at faint magnitudes and (Formula presented). The overall bias and scatter of our machine-learning based photo-zs satisfy the LSST Y1 requirement. We also use our photo-z to infer the ensemble redshift distribution (Formula presented). We study the photo-z improvement by including near-infrared photometry from the Euclid mission, and find that Euclid photometry improves photo-z at (Formula presented). Our results validate the RAIL pipeline for Rubin photo-z production and demonstrate promising initial performance.

AB - We present the first systematic analysis of photometric redshifts (photo-z) estimated from the Rubin Observatory Data Preview 1 (DP1) data taken with the Legacy Survey of Space and Time (LSST) Commissioning Camera. Employing the Redshift Assessment Infrastructure Layers (RAIL) framework, we apply eight photo-z algorithms to the DP1 photometry, using deep (Formula presented) coverage in the Extended Chandra Deep Field South (ECDFS) field and (Formula presented) data in the Rubin_SV_38_7 field. In the ECDFS field, we construct a reference catalogue from spectroscopic redshift (spec-z), grism redshift (grism-z), and multiband photo-z for training and validating photo-z. Performance metrics of the photo-z are evaluated using spec-zs from ECDFS and Dark Energy Spectroscopic Instrument Data Release 1 samples. Across the algorithms, we achieve per-galaxy photo-z scatter of (Formula presented) and outlier fractions around 10 per cent in the 6-band data, with performance degrading at faint magnitudes and (Formula presented). The overall bias and scatter of our machine-learning based photo-zs satisfy the LSST Y1 requirement. We also use our photo-z to infer the ensemble redshift distribution (Formula presented). We study the photo-z improvement by including near-infrared photometry from the Euclid mission, and find that Euclid photometry improves photo-z at (Formula presented). Our results validate the RAIL pipeline for Rubin photo-z production and demonstrate promising initial performance.

KW - galaxies: distances and redshifts

KW - methods: statistical

UR - https://www.scopus.com/pages/publications/105043832165

U2 - 10.1093/mnras/stag1012

DO - 10.1093/mnras/stag1012

M3 - Article

AN - SCOPUS:105043832165

SN - 0035-8711

VL - 550

JO - Monthly Notices of the Royal Astronomical Society

JF - Monthly Notices of the Royal Astronomical Society

IS - 2

M1 - stag1012

ER -

Photometric redshift estimation for Rubin Observatory Data Preview 1 with Redshift Assessment Infrastructure Layers

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