Abstract

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 Gaia DR2 parallaxes to infer distance and binarity. The method applies a neural network model trained on stars with precise Gaia parallax to the spectra and predicts K s-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., >5 kpc), the inclusion of constraints from spectroscopic significantly improves the distance estimates compared to inferences from Gaia parallax alone. Our method accommodates for emission-line stars by first identifying them via principal component analysis reconstructions and then treating them separately for the estimation. We also take into account unresolved binary/multiple stars, which we identify through deviations in the spectroscopic from the geometric inferred from Gaia parallax. This method of binary identification is particularly efficient for unresolved binaries with near equal-mass components and thus provides a useful supplementary way to identify unresolved binary or multiple-star systems. We present a catalog of spectroscopic , extinction, distance, flags for emission lines, and binary classification for 16,002 OB stars from LAMOST DR5. As an illustration, we investigate the of the enigmatic LB-1 system, which Liu et al. had argued consists of a B star and a massive stellar-mass black hole. Our results suggest that LB-1 is a binary system that contains two luminous stars with comparable brightness, and the result is further supported by parallax from the Gaia eDR3.

Highlights

  • O-type and B-type (OB) stars constitute the population of massive, young, and luminous stars in a galaxy

  • We take into account unresolved binary/multiple stars, which we identify through deviations in the spectroscopic MKs from the geometric MKs inferred from Gaia parallax

  • We have presented a data-driven approach for deriving Ks-band absolute magnitudes MKs for OB stars from low-resolution (R 1800) LAMOST spectra

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Summary

INTRODUCTION

For OB stars within 2 kpc from the Sun, high precision distance estimates are obtainable using parallaxes from. Stellar isochrones have been used to infer stellar absolute magnitudes and distances from stellar parameters Teff , log g , and [Fe/H] , as has been widely implemented on spectroscopic survey data sets (e.g. Carlin et al 2015; Yuan et al 2015b; Wang et al 2016; Xiang et al 2017a; Coronado et al 2018a; Green et al 2020; Queiroz et al 2020) This has been mostly applied to FGK stars, but it should provide decent distance estimates akin to the FGLR method for hot luminous stars. We apply the method to the LAMOST lowresolution (R 1800) spectra for a set of 16,002 OB stars from Liu et al (2019b), which is by far the most extensive set of spectra for luminous, hot stars We prefer this data-driven approach over the FGLR, given the complications of validating the accuracy of the stellar parameters determined for OB stars from low-resolution spectra.

METHOD OVERVIEW
IDENTIFYING EMISSION LINES WITH PCA RECONSTRUCTION
EXTINCTION
DATA-DRIVEN MKs ESTIMATION
The neural-network model
The training and test set
Measurement uncertainty and intrinsic uncertainty
BINARY IDENTIFICATION
DISTANCE
THE DISTANCE TO THE LB-1 B-STAR SYSTEM
Findings
SUMMARY

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