"Lite" version of Matias Carrasco-Kind's TPZ (Trees for Photo-z) regression-tree-based photo-z code. This initial version only implements the regression-tree mode, it does not implement the classification tree or SOM-based photo-z estimators. All credit on algorithm development and initial coding goes to Matias Carrasco-Kind.
If you use TPZ for any publication, in addition to RAIL, you should cite Matias' TPZ paper: Carrasco Kind, M., & Brunner, R. J., 2013 “TPZ : Photometric redshift PDFs and ancillary information by using prediction trees and random forests”, MNRAS, 432, 1483 Link
For more details on the algorith, see Matias's MLZ website: http://matias-ck.com/mlz/
For the regression tree mode, the current implementation includes generation of "random" data via Gaussian scatter on each of the attributes that contain an uncertainty, but it does not implement the out-of-bag error or varImportance sampling that are included in the full MLZ/TPZ package.
This package is part of the larger ecosystem of Photometric Redshifts in 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.
If you use this package, you should also cite the appropriate papers for each code used. A list of such codes is included in the Citing RAIL section of the main RAIL Read The Docs page.