T. Zhang, E. Charles, John Franklin Crenshaw, Samuel J. Schmidt, Prakruth Adari, J. Gschwend, S. Mau, Brett H. Andrews, É. Aubourg, Y. Bains, K. Bechtol, A. Boucaud, D. Boutigny, P. R. Burchat, J. Chevalier, J. Chiang, Hsu-Wen Chiang, Douglas Clowe, J. Cohen-Tanugi, C. Combet, Andrew J. Connolly, S. Dagoret-Campagne, Paul Daly, Felipe Daruich, Guillaume Daubard, J. De Vicente, H. Drass, K. Fanning, Eric Gawiser, M. L. Graham, L. P. Guy, Qianjun Hang, Patrick Ingraham, O. Ilbert, M. Jarvis, M. James Jee, Tim Jenness, A. S. Johnson, S Joudaki, Claire Juramy, S. M. Kahn, J. Bryce Kalmbach, Y. W. Kang, A. Kannawadi, L. S. Kelvin, Shuang Liang, Olivia Lynn, Nate B. Lust, Mostafa Lutfi, Alex I. Malz, R. Mandelbaum, Stuart Marshall, Joel Meyers, Myriam Migliore, M. Moniez, I Moskowitz, J. Neveu, J. A. Newman, E. Nourbakhsh, Drew Oldag, H Park, S. Pelesky, A A Plazas Malagón, Bruno Quint, Mubdi Rahman, A. Rasmussen, K. Reil, M Ricci, William Roby, A. Roodman, C. Roucelle, M. Salvato, B. Sánchez, David Sanmartim, R. H. Schindler, Jennifer Scora, Jacques Sebag, Nima Sedaghat, I. Sevilla-Noarbe, R. Shirley, Alsyha Shugart, Rance Solomon, Dan S. Taranu, G. Thayer, L. Toribio San Cipriano, Elana Urbach, Yousuke Utsumi, W. van Reeven, Anja von der Linden, C. W. Walter, W M Wood-Vasey, Z Zhang, J Zuntz
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 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 $\sigma _{\rm NMAD} \sim 0.03$ and outlier fractions around 10 per cent in the 6-band data, with performance degrading at faint magnitudes and $z\gt 1.2$. 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 $z\gt 1.2$. Our results validate the RAIL pipeline for Rubin photo-z production and demonstrate promising initial performance.