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

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arxiv 2510.07370 v1 pith:RBGSBUCI submitted 2025-10-08 astro-ph.IM astro-ph.GA

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

T. Zhang , E. Charles , J.F. Crenshaw , S.J. Schmidt , P. Adari , J. Gschwend , S. Mau , B. Andrews
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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 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 J. Neveu J.A. Newman E. Nourbakhsh D. Oldag H. Park S. Pelesky A.A. Plazas Malag\'on B. Quint M. Rahman A. Rasmussen K. Reil W. Roby A. Roodman C. Roucelle M. Salvato B. S\'anchez D. Sanmartim R.H. Schindler J. Scora J. Sebag N. Sedaghat I. Sevilla-Noarbe R. Shirley A. Shugart R. Solomon D. 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 J. Zuntz LSST Dark Energy Science Collaboration
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classification astro-ph.IM astro-ph.GA
keywords photo-zdataredshiftfieldrubinecdfsperformancephotometry
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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 $\sigma_{\rm NMAD} \sim 0.03$ 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.

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Cited by 5 Pith papers

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  2. Photometric Redshift PDFs via Neural Network Classification for DESI Legacy Imaging Surveys and Pan-STARRS

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    Neural network classification with CRPS optimization produces calibrated photometric redshift PDFs for DESI Legacy and Pan-STARRS data, achieving σ_NMAD of 0.0153 on LSDR10 and outperforming regression methods.

  3. A First Measurement of Circumgalactic Dust Reddening from Only 4.6 deg$^2$ of the Rubin Observatory's DP1

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    The first circumgalactic dust reddening measurement from Rubin DP1 data finds A_V proportional to r_perp to the -1.8 power within 120 kpc, consistent with prior SDSS/KiDS/DES results despite 1000x smaller area and fai...

  4. Hyrax: An Extensible Framework for Rapid ML Experimentation and Unsupervised Discovery in the Era of Rubin, Roman, and Euclid

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  5. Infrared-enhanced Photometric Redshifts for the Dark Energy Survey Y6 Gold catalogue

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    DES+WISE W1/W2 photometry significantly reduces photo-z scatter, bias and outliers versus optical-only DES, while VHS adds only marginal gains at z<1.5.