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Predicting the Spectroscopic Features of Galaxies by Applying Manifold Learning on Their Broadband Colors: Proof of Concept and Potential Applications for Euclid, Roman, and Rubin LSST

Crossref DOI link: https://doi.org/10.3847/1538-4357/ad38b8

Published Online: 2024-05-17

Published Print: 2024-05-01

Update policy: https://doi.org/10.1088/crossmark-policy

Authors

Jafariyazani, Marziye https://orcid.org/0000-0001-8019-6661
Masters, Daniel https://orcid.org/0000-0001-5382-6138
Faisst, Andreas L. https://orcid.org/0000-0002-9382-9832
Teplitz, Harry I. https://orcid.org/0000-0002-7064-5424
Ilbert, Olivier https://orcid.org/0000-0002-7303-4397
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Article Title: Predicting the Spectroscopic Features of Galaxies by Applying Manifold Learning on Their Broadband Colors: Proof of Concept and Potential Applications for Euclid, Roman, and Rubin LSST

Journal Title: The Astrophysical Journal

Article Type: paper

Copyright Information: © 2024. The Author(s). Published by the American Astronomical Society.

Publication dates

Date Received: 2023-11-28

Date Accepted: 2024-03-26

Online publication date: 2024-05-17

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