Photometric Classification of Evolved Massive Stars: Spectroscopic Verification and Validation

Abstract

Dorn-Wallenstein et al. utilized a novel machine-learning technique to classify a large sample of evolved massive stars. This resulted in new classifications for ∼2550 objects. We wish to validate the efficiency of the Dorn-Wallenstein et al. machine classifier. To this end we obtained new observations of four stars identified by Dorn-Wallenstein et al., with a focus on verifying newly identified emission-line objects and evolved supergiants. We identified a previously unconfirmed Be star, TYC 3740-1791-1, using these data. We assigned spectral types to the two stars in our sample with sufficient signal-to-noise data. We then used Gaia DR3 BP/RP spectra to validate an additional 73 stars from Dorn-Wallenstein et al. Our classifications support the completeness and contamination reported by the authors and confirm the validity of using machine learning-based classification methods on massive stars in the era of big data.