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arXiv · 2505.02928

Redshift Assessment Infrastructure Layers (RAIL): Rubin-era photometric redshift stress-testing and at-scale production

The RAIL Team·Jan Luca van den Busch·Eric Charles·Johann Cohen-Tanugi·Alice Crafford·John Franklin Crenshaw·Sylvie Dagoret·Josue De-Santiago·Juan De Vicente·Qianjun Hang·Benjamin Joachimi·Shahab Joudaki·J. Bryce Kalmbach·Arun Kannawadi·Shuang Liang·Olivia Lynn·Alex I. Malz·Rachel Mandelbaum·Grant Merz·Irene Moskowitz·Drew Oldag·Jaime Ruiz-Zapatero·Mubdi Rahman·Markus M. Rau·Samuel J. Schmidt·Jennifer Scora·Raphael Shirley·Benjamin Stölzner·Laura Toribio San Cipriano·Luca Tortorelli·Ziang Yan·Tianqing Zhang·the LSST Dark Energy Science Collaboration
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Abstract

Virtually all extragalactic use cases of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) require the use of galaxy redshift information, yet the vast majority of its sample of tens of billions of galaxies will lack high-fidelity spectroscopic measurements thereof, instead relying on photometric redshifts (photo-$z$) subject to systematic imprecision and inaccuracy best encapsulated by photo-$z$ probability density functions (PDFs). We present the version 1 release of Redshift Assessment Infrastructure Layers (RAIL), an open source Python library for at-scale probabilistic photo-$z$ estimation, initiated by the LSST Dark Energy Science Collaboration (DESC) with contributions from the LSST Interdisciplinary Network for Collaboration and Computing (LINCC) Frameworks team. RAIL's three subpackages provide modular tools for end-to-end stress-testing, including a forward modeling suite to generate realistically complex photometry, a unified API for estimating per-galaxy and ensemble redshift PDFs by an extensible set of algorithms, and built-in metrics of both photo-$z$ PDFs and point estimates. RAIL serves as a flexible toolkit enabling the derivation and optimization of photo-$z$ data products at scale for a variety of science goals and is not specific to LSST data. We thus describe to the extragalactic science community, including and beyond Rubin the design and functionality of the RAIL software library so that any researcher may have access to its wide array of photo-$z$ characterization and assessment tools.

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The RAIL Team, Jan Luca van den Busch, Eric Charles, Johann Cohen-Tanugi, Alice Crafford, John Franklin Crenshaw, Sylvie Dagoret, Josue De-Santiago, Juan De Vicente, Qianjun Hang, Benjamin Joachimi, Shahab Joudaki, J. Bryce Kalmbach, Arun Kannawadi, Shuang Liang, Olivia Lynn, Alex I. Malz, Rachel Mandelbaum, Grant Merz, Irene Moskowitz, Drew Oldag, Jaime Ruiz-Zapatero, Mubdi Rahman, Markus M. Rau, Samuel J. Schmidt, Jennifer Scora, Raphael Shirley, Benjamin Stölzner, Laura Toribio San Cipriano, Luca Tortorelli, Ziang Yan, Tianqing Zhang, the LSST Dark Energy Science Collaboration. 2026-03-12. Redshift Assessment Infrastructure Layers (RAIL): Rubin-era photometric redshift stress-testing and at-scale production. https://doi.org/10.33232/001c.158200

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