Redshift Assessment Infrastructure Layers (RAIL): Rubin-era photometric redshift stress-testing and at-scale production
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-) subject to systematic imprecision and inaccuracy best encapsulated by photo- 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- 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- PDFs and point estimates. RAIL serves as a flexible toolkit enabling the derivation and optimization of photo- 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- characterization and assessment tools.
Keywords
Cite
@article{arxiv.2505.02928,
title = {Redshift Assessment Infrastructure Layers (RAIL): Rubin-era photometric redshift stress-testing and at-scale production},
author = {The RAIL Team and Jan Luca van den Busch and Eric Charles and Johann Cohen-Tanugi and Alice Crafford and John Franklin Crenshaw and Sylvie Dagoret and Josue De-Santiago and Juan De Vicente and Qianjun Hang and Benjamin Joachimi and Shahab Joudaki and J. Bryce Kalmbach and Arun Kannawadi and Shuang Liang and Olivia Lynn and Alex I. Malz and Rachel Mandelbaum and Grant Merz and Irene Moskowitz and Drew Oldag and Jaime Ruiz-Zapatero and Mubdi Rahman and Markus M. Rau and Samuel J. Schmidt and Jennifer Scora and Raphael Shirley and Benjamin Stölzner and Laura Toribio San Cipriano and Luca Tortorelli and Ziang Yan and Tianqing Zhang and the LSST Dark Energy Science Collaboration},
journal= {arXiv preprint arXiv:2505.02928},
year = {2026}
}
Comments
Accepted by OJAp, 21 pages, 6 figures, 5 tables