Paper

Photometric Redshift Estimation for Rubin Observatory Data Preview 1 with Redshift Assessment Infrastructure Layers (RAIL)

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

2025

Overview

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 \\(_ NMAD 0.03\\) 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.

• Publisher: Cornell University Library, arXiv.org
• Subject: Algorithms / Dark energy / Infrared photometry / Infrastructure / Machine learning / Observatories / Outliers (statistics) / Performance measurement / Photometry / Red shift / Scattering