sompz - RAIL estimator, summarizer, and classifier using the SOMPZ method described in Buchs, Davis, et al. 2019, Sánchez, Raveri, Alarcon, Bernstein 2020, Myles, Alarcon et al. 2021 and Campos, et al. 2024.
The main product is the galaxy ensemble tomographic bin assignments and associated redshift distributions qp ensemble. The code additionally saves the two Self-Organizing Maps (SOMs) constructed for
The SOMPZ algorithm generates redshift distributions for a sample of galaxies with a multi-step inference formalism. Based on observations of a wide-field imaging dataset catalog and a deep-field imaging dataset catalog (traditionally lower-noise optical bands and additional near-infrared bands), the algorithm takes three primary tabular data inputs:
spec_data: a catalog with secure redshifts, deep-field photometry, and simulated wide-field photometrybalrog_data: a catalog with deep-field photometry and simulated wide-field photometrywide_data: a catalog with wide-field photometry
In practice, spec_data is a subset of balrog_data.
These catalogs are used to train two SOMs: one built with deep-field photometry and the other built with wide-field photometry.
Once all samples are assigned to the wide SOM and spec_data and balrog_data are assigned to the deep SOM the wide SOM cells can be grouped into tomographic bins via a tomographic binning algorithm. The redshift distributions are computed as follows:
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 instructions are available under Installation on ReadTheDocs.
The greatest strength of RAIL is its extensibility; those interested in contributing to RAIL should start by consulting the Contributing guidelines on ReadTheDocs.
RAIL is open source and may be used according to the terms of its LICENSE (BSD 3-Clause). If you make use of the ideas or software here in any publication, you must cite this repository https://github.com/LSSTDESC/RAIL as "LSST-DESC PZ WG (in prep)" with the Zenodo DOI. Please consider also inviting the developers as co-authors on publications resulting from your use of RAIL by making an issue. 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.