A Robust Hot Subdwarfs Identification Method Based on Deep Learning
  Hot subdwarf star is a particular type of star that is crucial for studying
binary evolution and atmospheric diffusion processes. In recent years,
identifying Hot subdwarfs by machine learning methods has become a hot topic,
but there are still limitations in automation and accuracy. In this paper, we
proposed a robust identification method based on the convolutional neural
network (CNN). We first constructed the dataset using the spectral data of
LAMOS DR7-V1. We then constructed a hybrid recognition model including an
8-class classification model and a binary classification model. The model
achieved an accuracy of 96.17% on the testing set. To further validate the
accuracy of the model, we selected 835 Hot subdwarfs that were not involved in
the training process from the identified LAMOST catalog (2428, including
repeated observations) as the validation set. An accuracy of 96.05% was
achieved. On this basis, we used the model to filter and classify all
10,640,255 spectra of LAMOST DR7-V1, and obtained a catalog of 2393 Hot
subdwarf candidates, of which 2067 have been confirmed. We found 25 new Hot
subdwarfs among the remaining candidates by manual validation. The overall
accuracy of the model is 87.42%. Overall, the model presented in this study can
effectively identify specific spectra with robust results and high accuracy,
and can be further applied to the classification of large-scale spectra and the
search of specific targets.
