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A plain-language guide to alpha element abundances in the Milky Way galaxy

Published on:

23 January 2022

Primary Category:

Solar and Stellar Astrophysics

Paper Authors:

Alvin Gavel,

René Andrae,

Morgan Fouesneau,

Andreas J. Korn,

Rosanna Sordo

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Key Details

Machine learning models were developed to estimate alpha abundances from low-resolution Gaia spectra

Models trained on real spectra had some success, but failed when tested on simulated spectra

This implies the models used indirect correlations, not the direct spectral signature

So this method cannot reliably identify stars differing only in alpha abundance

AI generated summary

A plain-language guide to alpha element abundances in the Milky Way galaxy

This paper developed machine learning models to estimate alpha element abundances in stars using low-resolution spectra from the Gaia space telescope. The models were somewhat successful when trained on real data, but failed on simulated spectra, implying they relied on indirect correlations rather than a direct spectral signature. Overall the study concluded that this approach cannot reliably identify stars differing only in alpha abundance.

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