A Robust Hot Subdwarfs Identification Method Based on Deep Learning

TL;DR

This paper presents a deep learning-based method using CNNs for accurate and automated identification of hot subdwarf stars from spectral data, significantly improving detection efficiency and accuracy.

Contribution

It introduces a hybrid CNN model for hot subdwarf identification, achieving over 96% accuracy and enabling large-scale spectral classification.

Findings

Achieved 96.17% accuracy on test set

Validated 96.05% accuracy on independent sample

Discovered 25 new hot subdwarfs through manual validation

Abstract

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.