Templates from FORS2 observations to improve photometric redshifts estimation

Presentation of a method to fit the Star Formation History of observed galaxies and use it to improve the SED templates in photometric redshifts estimation algorithms. This presentation was given at the LSST@Europe6 meeting in La Palme (Spain), during the DESC session on September 18, 2024. It focusses on the method used to build SFH/SED templates from a collection of observed spectroscopic data and photometric catalogs, and includes an overview of preliminary results and future work to select the most appropriate galaxies templates for the estimation of the photometric redshifts of objects observed by Rubin/LSST.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-09-26
DOI
https://doi.org/10.5281/zenodo.13842406
Primary Topic
Paleontology and Evolutionary Biology
Type
article
Field-Weighted Citation Impact
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article

Templates from FORS2 observations to improve photometric redshifts estimation

Zenodo (CERN European Organization for Nuclear Research)
Paleontology and Evolutionary Biology
article

Templates from FORS2 observations to improve photometric redshifts estimation

article en

Abstract

Presentation of a method to fit the Star Formation History of observed galaxies and use it to improve the SED templates in photometric redshifts estimation algorithms. This presentation was given at the LSST@Europe6 meeting in La Palme (Spain), during the DESC session on September 18, 2024. It focusses on the method used to build SFH/SED templates from a collection of observed spectroscopic data and photometric catalogs, and includes an overview of preliminary results and future work to select the most appropriate galaxies templates for the estimation of the photometric redshifts of objects observed by Rubin/LSST.

Zenodo (CERN European Organization for Nuclear Research)
Centre National de la Recherche Scientifique (FR), Laboratoire de Physique des 2 Infinis Irène Joliot-Curie (FR)
Openalex Percentile: Top 42%
Paleontology and Evolutionary Biology
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