A robust method for identifying Be stars in the LAMOST Data Release 11 based on Deep-learning approach

TL;DR

This paper presents a deep learning-based method to identify Be stars in LAMOST DR11 data, achieving high accuracy and discovering thousands of new Be stars, including classifications into Herbig and Classical types.

Contribution

The study introduces a novel deep learning classification model combining LSTM and CNN for Be star identification in large spectroscopic datasets.

Findings

Achieved 97.86% testing accuracy in classifying B-type stars.

Identified 8,298 Be stars, including 4,511 new discoveries.

Classified Be stars into Herbig and Classical types with color information.

Abstract

Be stars are rapidly rotating B-type stars that exhibit Balmer emission lines in their optical spectra. These stars play an important role in studies of stellar evolution and disk structures. In this work, we carried out a systematic search for Be stars based on LAMOST spectroscopic data. Using low-resolution spectra from LAMOST DR11, we constructed a data set and developed a classification model that combines long short-term memory networks and convolutional neural networks , achieving a testing accuracy of 97.86%. The trained model was then applied to spectra with signal-to-noise ratios greater than 10, yielding 55,667 B-type candidates. With the aid of the MKCLASS automated classification tool and manual verification, we finally confirmed 40,223 B-type spectra. By cross-matching with published H{\alpha} emission-line star catalogs, we obtained a sample of 8298 Be stars, including 3787 previously reported Be stars and 4511 newly discovered. Furthermore, by incorporating color information, we classified the Be star sample into Herbig Be stars and Classical Be stars. In total, we identified 3363 Classical Be stars and 35 Herbig Be stars. The B-type and Be star catalogs derived in this study, together with the code used for model training, have been publicly released to facilitate community research.