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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| Hoofdauteurs: | , , , , |
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| Formaat: | Artigo |
| Taal: | Inglês |
| Gepubliceerd in: |
IOP Publishing
2024-01-01
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| Reeks: | The Astrophysical Journal |
| Onderwerpen: | |
| Online toegang: | https://doi.org/10.3847/1538-4357/ad38b8 |
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