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Researchers Propose a Robust Identification Method to Identify the Hot Subdwarfs based on the Convolutional Neural Network
Author: | Update time:2022-02-28           | Print | Close | Text Size: A A A

Prof. DENG Linhua from Yunnan Observatories of the Chinese Academy of Sciences with collaborators (Prof. WANG Feng and Lei Tan from Guangzhou University and teams from National Astronomical Observatories of the Chinese Academy of Sciences and China West Normal University), proposed a robust identification method to identify the hot subdwarfs based on a convolutional neural network. The model presented in their 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 for specific targets.

This research, published by The Astrophysical Journal Supplement Series, first constructed the data set using the spectral data of LAMOST DR7-V1, and then constructed a hybrid recognition model including an eight-class classification model and a binary classification model. The authors selected 835 hot subdwarfs that were not involved in the training process from the identified LAMOST catalog as the validation set.

Hot subdwarfs are core helium-burning stars located below the upper main sequence of the Hertzsprung–Russell diagram and are referred to as extreme horizontal branch stars because of their evolution stage. Driven by the scientific research, searching for and identifying hot subdwarfs and constructing hot subdwarf catalogs have become popular topics in hot subdwarf research. The traditional method of searching for subdwarfs is mainly based on the basic characteristics of hot subdwarfs. However, the traditional identification methods mainly rely on manual processing, which is laborious and difficult to use to meet the demands of handling large-scale spectral data.

Identifying hot subdwarfs from the LAMOST catalog has essential research value because LAMOST can reveal the spectral characteristics of hot subdwarfs that show details of their formation and evolution. Identifying hot subdwarfs based on deep learning can significantly reduce the difficulty of manual identification and obtain credible results. It can be considered as one of the most effective methods to search for a specific target in a large amount of catalog data. In their work, a hybrid model with an eight-class classification model and a binary classification model was proposed and applied to search for hot subdwarfs in the LAMOST catalog.

Based on the model, they filtered and classified all 10,640,255 spectra of LAMOST DR7-V1, and obtained a catalog of 2393 hot subdwarf candidates, of which 2067 have been confirmed. They found 25 new hot subdwarfs among the remaining candidates by manual validation.


DENG Linhua, Yunnan Observatories, Chinese Academy of Sciences

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