Identification of new classical Be stars from the LAMOST MRS survey

TL;DR

This paper uses a deep learning approach with ResNet to identify classical Be stars from the LAMOST MRS survey, discovering 183 new candidates and analyzing their properties and kinematics.

Contribution

It introduces a novel application of deep convolutional neural networks for Be star identification in large spectroscopic surveys, achieving high accuracy and discovering new candidates.

Findings

Identified 183 new classical Be stars from the LAMOST MRS survey.

Achieved 99.5% classification accuracy with ResNet neural network.

Discovered 16 new runaway Be stars based on Gaia kinematics.

Abstract

Be stars are B-type main-sequence stars that display broad Balmer emission lines in their spectra. Identification of Be population is essential to further examine the formation and evolutionary models. We report the detection of classical Be (CBe) stars from observations with the Large sky Area Multi-Object fiber Spectroscopic Telescope Medium Resolution Survey of Date Release 7 (LAMOST MRS DR7). We used a deep convolutional neural network, the ResNet, with an 18-layer module to examine the morphology of the H alpha profile. We identified 1,162 candidate Be stars from the collection of 2,260,387 spectra for 789,918 stars in the database. The ResNet network achieves a Be star classification accuracy of 99.5%. Among the detections, 151 of these are prior known Be stars cross-matched from the literature. By applying a three-step test, we identified 183 new CBe stars. We find that 41 CBe stars are members of known open clusters. Based upon an investigation of the kinematics of the identified CBe stars from the Gaia EDR3 astrometric solutions, we identified 16 new runaways. These new identifications will provide a reference for future follow-ups to further investigate their physical properties.