Closing the Stellar Labels Gap: Stellar Label independent Evidence for [α/M] Information in Gaia BP/RP Spectra

Data-driven models for stellar spectra that depend on stellar labels suffer from label systematics which decrease model performance: the stellar labels gap. To close the stellar labels gap, we present a stellar label independent model for Gaia BP/RP spectra. We develop a novel implementation of a va...

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Main Authors: Alexander Laroche, Joshua S. Speagle
Format: Article
Language:English
Published: IOP Publishing 2025-01-01
Series:The Astrophysical Journal
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Online Access:https://doi.org/10.3847/1538-4357/ad9607
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author Alexander Laroche
Joshua S. Speagle
author_facet Alexander Laroche
Joshua S. Speagle
author_sort Alexander Laroche
collection DOAJ
description Data-driven models for stellar spectra that depend on stellar labels suffer from label systematics which decrease model performance: the stellar labels gap. To close the stellar labels gap, we present a stellar label independent model for Gaia BP/RP spectra. We develop a novel implementation of a variational auto-encoder, which learns to generate an XP spectrum and accompanying scatter without relying on stellar labels. We demonstrate that our model achieves competitive XP spectra reconstructions in comparison to stellar label dependent models. We find that our model learns stellar properties directly from the data itself. We then apply our model to XP/APOGEE giant stars to study the [ α / M ] information in Gaia XP. We provide strong evidence that the XP spectra contain meaningful [ α / M ] information by demonstrating that our model learns the α -bimodality, without relying on stellar label correlations for stars with T _eff  < 5000 K, while also being sensitive to the anomalous abundances of Gaia-Enceladus stars. We have publicly released our trained model, codebase and data. Importantly, our stellar label independent model can be implemented for any and all XP spectra because our model's performance scales with training object density, not training label density.
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spelling doaj-art-c2a556ef14454682aa7433d5c2d89ef72025-08-20T02:34:09ZengIOP PublishingThe Astrophysical Journal1538-43572025-01-019791510.3847/1538-4357/ad9607Closing the Stellar Labels Gap: Stellar Label independent Evidence for [α/M] Information in Gaia BP/RP SpectraAlexander Laroche0https://orcid.org/0000-0002-5522-0217Joshua S. Speagle1https://orcid.org/0000-0003-2573-9832David A. Dunlap Department of Astronomy & Astrophysics, University of Toronto , 50 Saint George Street, Toronto, ON M5S 3H4, Canada; Dunlap Institute for Astronomy & Astrophysics, University of Toronto , 50 Saint George Street, Toronto, ON M5S 3H4, CanadaDavid A. Dunlap Department of Astronomy & Astrophysics, University of Toronto , 50 Saint George Street, Toronto, ON M5S 3H4, Canada; Dunlap Institute for Astronomy & Astrophysics, University of Toronto , 50 Saint George Street, Toronto, ON M5S 3H4, Canada; Department of Statistical Sciences, University of Toronto , 9th Floor, Ontario Power Building, 700 University Avenue, Toronto, ON M5G 1Z5, Canada; Data Sciences Institute, University of Toronto , 17th Floor, Ontario Power Building, 700 University Avenue, Toronto, ON M5G 1Z5, CanadaData-driven models for stellar spectra that depend on stellar labels suffer from label systematics which decrease model performance: the stellar labels gap. To close the stellar labels gap, we present a stellar label independent model for Gaia BP/RP spectra. We develop a novel implementation of a variational auto-encoder, which learns to generate an XP spectrum and accompanying scatter without relying on stellar labels. We demonstrate that our model achieves competitive XP spectra reconstructions in comparison to stellar label dependent models. We find that our model learns stellar properties directly from the data itself. We then apply our model to XP/APOGEE giant stars to study the [ α / M ] information in Gaia XP. We provide strong evidence that the XP spectra contain meaningful [ α / M ] information by demonstrating that our model learns the α -bimodality, without relying on stellar label correlations for stars with T _eff  < 5000 K, while also being sensitive to the anomalous abundances of Gaia-Enceladus stars. We have publicly released our trained model, codebase and data. Importantly, our stellar label independent model can be implemented for any and all XP spectra because our model's performance scales with training object density, not training label density.https://doi.org/10.3847/1538-4357/ad9607Fundamental parameters of starsStellar abundancesAstrostatistics techniquesAstronomy data analysis
spellingShingle Alexander Laroche
Joshua S. Speagle
Closing the Stellar Labels Gap: Stellar Label independent Evidence for [α/M] Information in Gaia BP/RP Spectra
The Astrophysical Journal
Fundamental parameters of stars
Stellar abundances
Astrostatistics techniques
Astronomy data analysis
title Closing the Stellar Labels Gap: Stellar Label independent Evidence for [α/M] Information in Gaia BP/RP Spectra
title_full Closing the Stellar Labels Gap: Stellar Label independent Evidence for [α/M] Information in Gaia BP/RP Spectra
title_fullStr Closing the Stellar Labels Gap: Stellar Label independent Evidence for [α/M] Information in Gaia BP/RP Spectra
title_full_unstemmed Closing the Stellar Labels Gap: Stellar Label independent Evidence for [α/M] Information in Gaia BP/RP Spectra
title_short Closing the Stellar Labels Gap: Stellar Label independent Evidence for [α/M] Information in Gaia BP/RP Spectra
title_sort closing the stellar labels gap stellar label independent evidence for α m information in gaia bp rp spectra
topic Fundamental parameters of stars
Stellar abundances
Astrostatistics techniques
Astronomy data analysis
url https://doi.org/10.3847/1538-4357/ad9607
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