Photometric Redshift Estimation for Rubin Observatory Data Preview 1 with Redshift Assessment Infrastructure Layers (RAIL)
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 ugrizy coverage in the Extended Chandra Deep Field South (ECDFS) field and griz data in the Rubin_SV_38_7 field. In the ECDFS field, we construct a reference catalog 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 and outlier fractions around 10% in the 6-band data, with performance degrading at faint magnitudes and z>1.2. 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 n(z). We study the photo-z improvement by including near-infrared photometry from the Euclid mission, and find that Euclid photometry improves photo-z at z>1.2. Our results validate the RAIL pipeline for Rubin photo-z production and demonstrate promising initial performance.
Cite
@article{arxiv.2510.07370,
title = {Photometric Redshift Estimation for Rubin Observatory Data Preview 1 with Redshift Assessment Infrastructure Layers (RAIL)},
author = {T. Zhang and E. Charles and J. F. Crenshaw and S. J. Schmidt and P. Adari and J. Gschwend and S. Mau and B. Andrews 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 H. -F. Chiang and D. Clowe and J. Cohen-Tanugi and C. Combet and A. Connolly and S. Dagoret-Campagne and P. N. Daly and F. Daruich and G. Daubard and J. De Vicente and H. Drass and K. Fanning and E. Gawiser and M. Graham and L. P. Guy and Q. Hang and P. Ingraham and O. Ilbert and M. Jarvis and M. J. Jee and T. Jenness and A. Johnson and C. Juramy-Gilles and S. M. Kahn and J. B. Kalmbach and Y. Kang and A. Kannawadi and L. S. Kelvin and S. Liang and O. Lynn and N. B. Lust and M. Lutfi and A. Malz and R. Mandelbaum and S. Marshall and J. Meyers and M. Migliore and M. Moniez and J. Neveu and J. A. Newman and E. Nourbakhsh and D. Oldag and H. Park and S. Pelesky and A. A. Plazas Malagón and B. Quint and M. Rahman and A. Rasmussen and K. Reil and W. Roby and A. Roodman and C. Roucelle and M. Salvato and B. Sánchez and D. Sanmartim and R. H. Schindler and J. Scora and J. Sebag and N. Sedaghat and I. Sevilla-Noarbe and R. Shirley and A. Shugart and R. Solomon and D. Taranu and G. Thayer and L. Toribio San Cipriano and E. Urbach and Y. Utsumi and W. van Reeven and A. von der Linden and C. W. Walter and W. M. Wood-Vasey and J. Zuntz and LSST Dark Energy Science Collaboration},
journal= {arXiv preprint arXiv:2510.07370},
year = {2025}
}
Comments
14 pages, 8 figures, submitted to MNRAS