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

Entering the era of large-scale galaxy surveys, which will deliver unprecedented amounts of photometric and spectroscopic data, there is a growing need for more efficient, data-driven, and less model-dependent techniques to analyze the spectral energy distribution of galaxies. In this work, we demon...

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書誌詳細

出版年:	The Astrophysical Journal
主要な著者:	Marziye Jafariyazani, Daniel Masters, Andreas L. Faisst, Harry I. Teplitz, Olivier Ilbert
フォーマット:	論文
言語:	英語
出版事項:	IOP Publishing 2024-01-01
主題:	Broad band photometry Spectroscopy Galaxy colors
オンライン･アクセス:	https://doi.org/10.3847/1538-4357/ad38b8