Hunting for C-rich long-period variable stars in the Milky Way's bar-bulge using unsupervised classification of Gaia BP/RP spectra
Jason L. Sanders, Noriyuki Matsunaga

TL;DR
This study uses unsupervised learning on Gaia BP/RP spectra to classify long-period variable stars as oxygen- or carbon-rich, revealing insights into their distribution and origins in the Milky Way's bar-bulge.
Contribution
It introduces an unsupervised classification method for C-rich and O-rich stars using Gaia spectra and validates photometric classification with high purity, providing new insights into stellar populations.
Findings
Spectra separate into O-rich and C-rich groups despite dust.
Small fraction of C-rich stars found in the bar-bulge with long periods.
Classifications suggest stars are likely from binary evolution, not young star formation.
Abstract
The separation of oxygen- and carbon-rich AGB sources is crucial for their accurate use as local and cosmological distance and age/metallicity indicators. We investigate the use of unsupervised learning algorithms for classifying the chemistry of long-period variables from Gaia DR3's BP/RP spectra. Even in the presence of significant interstellar dust, the spectra separate into two groups attributable to O-rich and C-rich sources. Given these classifications, we utilise a supervised approach to separate O-rich and C-rich sources without BP/RP spectra but instead given broadband optical and infrared photometry finding a purity of our C-rich classifications of around per cent. We test and validate the classifications against other advocated colour-colour separations based on photometry. Furthermore, we demonstrate the potential of BP/RP spectra for finding S-type stars or those…
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Taxonomy
TopicsStellar, planetary, and galactic studies · Astronomy and Astrophysical Research · Astronomical Observations and Instrumentation
