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The many flavours of photometric redshifts

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Salvato, Mara, Ilbert, Olivier, Hoyle, Ben·2018·Nature Astronomy

DOI 10.1038/s41550-018-0478-0

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Authors: Salvato, M., Ilbert, O., & Hoyle, B.
Affiliations: Max Planck Institute for Extraterrestrial Physics (DE)
Laboratoire d’Astrophysique de Marseille (FR)
Corresponding author: Salvato, Mara
Ilbert, Olivier
Hoyle, Ben
ORCID: Salvato, Mara
Ilbert, Olivier
Hoyle, Ben
Venue: Nature Astronomy
Publisher: Springer Science and Business Media LLC
Published: 2018-06-04
Volume: 3
Issue: 3
Pages: 212-222
ISSN: 2397-3366 (ISSN-L)
DOI: 10.1038/s41550-018-0478-0
OpenAlex: W2963284157
Cited by: 234
References: 156
Citations by year: 2026: 23 · 2025: 31 · 2024: 36 · 2023: 25 · 2022: 35 · 2021: 35 · 2020: 22 · 2019: 25 · 2018: 2
FWCI: 11.30
Type: journal article
Language: en
Open access: green
arxiv.org
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Nature Astronomy (publishedVersion)
arXiv (Cornell University) (submittedVersion)
HAL (submittedVersion, other-oa)
MPG.PuRe (Max Planck Society) (submittedVersion)
arXiv (Cornell University)
Topics: Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
Physical Sciences › Physics and Astronomy › Instrumentation › Astronomy and Astrophysical Research
Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Stellar, planetary, and galactic studies
Keywords: photometric redshifts, cosmological distances, template fitting, machine learning, redshift accuracy, survey strategy, wide-field survey, dark energy, stellar mass assembly, galaxy evolution

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