Data-driven spectroscopic estimates of absolute magnitude, distance and binarity -- method and catalog of 16,002 O- and B-type stars from LAMOST
Mao-Sheng Xiang, Hans-Walter Rix, Yuan-Sen Ting, Eleonora Zari, Kareem, El-Badry, Hai-Bo Yuan, Wen-Yuan Cui

TL;DR
This paper introduces a neural network-based method to estimate absolute magnitudes, distances, and binarity of O- and B-type stars from LAMOST spectra, improving distance accuracy especially for distant stars and identifying unresolved binaries.
Contribution
The paper presents a novel data-driven approach combining LAMOST spectra and Gaia data to accurately estimate stellar parameters and identify unresolved binaries in a large star catalog.
Findings
Achieved 0.25 mag precision in absolute magnitude estimation.
Improved distance estimates for stars beyond 5 kpc.
Effectively identified unresolved binary systems.
Abstract
We present a data-driven method to estimate absolute magnitudes for O- and B-type stars from the LAMOST spectra, which we combine with {\it Gaia} parallaxes to infer distance and binarity. The method applies a neural network model trained on stars with precise {\it Gaia} parallax to the spectra and predicts -band absolute magnitudes with a precision of 0.25\,mag, which corresponds to a precision of 12\% in spectroscopic distance. For distant stars (e.g. \,kpc), the inclusion of constraints from spectroscopic significantly improves the distance estimates compared to inferences from {\it Gaia} parallax alone. Our method accommodates for emission line stars by first identifying them via PCA reconstructions and then treating them separately for the estimation. We also take into account unresolved binary/multiple stars, which we identify through…
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