pz-rail-sklearn 2.0.0

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RAIL sklearn

RAIL estimators using scikit-learn regressors

RAIL: Redshift Assessment Infrastructure Layers

RAIL is a flexible software library providing tools to produce at-scale photometric redshift data products, including uncertainties and summary statistics, and stress-test them under realistically complex systematics. A detailed description of RAIL's modular structure is available in the Overview on ReadTheDocs.

RAIL serves as the infrastructure supporting many extragalactic applications of the Legacy Survey of Space and Time (LSST) on the Vera C. Rubin Observatory, including Rubin-wide commissioning activities. RAIL was initiated by the Photometric Redshifts (PZ) Working Group (WG) of the LSST Dark Energy Science Collaboration (DESC) as a result of the lessons learned from the Data Challenge 1 (DC1) experiment to enable the PZ WG Deliverables in the LSST-DESC Science Roadmap (see Sec. 5.18), aiming to guide the selection and implementation of redshift estimators in DESC analysis pipelines. RAIL is developed and maintained by a diverse team comprising DESC Pipeline Scientists (PSs), international in-kind contributors, LSST Interdisciplinary Collaboration for Computing (LINCC) Frameworks software engineers, and other volunteers, but all are welcome to join the team regardless of LSST data rights.

Installation

Installation instructions are available under Installation on ReadTheDocs.

Contributing

The greatest strength of RAIL is its extensibility; those interested in contributing to RAIL should start by consulting the Contributing guidelines on ReadTheDocs.

Citing RAIL

RAIL is open source and may be used according to the terms of its LICENSE (BSD 3-Clause). If you used RAIL in your study, please cite this repository https://github.com/LSSTDESC/RAIL, and RAIL Team et al. (2025) https://arxiv.org/abs/2505.02928

@ARTICLE{2025arXiv250502928T,
       author = {{The RAIL Team} and {van den Busch}, Jan Luca and {Charles}, Eric and {Cohen-Tanugi}, Johann and {Crafford}, Alice and {Crenshaw}, John Franklin and {Dagoret}, Sylvie and {De-Santiago}, Josue and {De Vicente}, Juan and {Hang}, Qianjun and {Joachimi}, Benjamin and {Joudaki}, Shahab and {Bryce Kalmbach}, J. and {Kannawadi}, Arun and {Liang}, Shuang and {Lynn}, Olivia and {Malz}, Alex I. and {Mandelbaum}, Rachel and {Merz}, Grant and {Moskowitz}, Irene and {Oldag}, Drew and {Ruiz-Zapatero}, Jaime and {Rahman}, Mubdi and {Rau}, Markus M. and {Schmidt}, Samuel J. and {Scora}, Jennifer and {Shirley}, Raphael and {St{\"o}lzner}, Benjamin and {Toribio San Cipriano}, Laura and {Tortorelli}, Luca and {Yan}, Ziang and {Zhang}, Tianqing and {the Dark Energy Science Collaboration}},
        title = "{Redshift Assessment Infrastructure Layers (RAIL): Rubin-era photometric redshift stress-testing and at-scale production}",
      journal = {arXiv e-prints},
     keywords = {Instrumentation and Methods for Astrophysics, Cosmology and Nongalactic Astrophysics, Astrophysics of Galaxies},
         year = 2025,
        month = may,
          eid = {arXiv:2505.02928},
        pages = {arXiv:2505.02928},
          doi = {10.48550/arXiv.2505.02928},
archivePrefix = {arXiv},
       eprint = {2505.02928},
 primaryClass = {astro-ph.IM},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2025arXiv250502928T},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

Please consider also inviting the developers as co-authors on publications resulting from your use of RAIL by making an issue. A convenient list of what to cite may be found under Citing RAIL on ReadTheDocs. Additionally, several of the codes accessible through the RAIL ecosystem must be cited if used in a publication.

Additionally, several of the codes accessible through the RAIL ecosystem must be cited if used in a publication. A convenient list of what to cite may be found under Citing RAIL on ReadTheDocs.