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

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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 $ugrizy$ 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.

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