Study on Stellar Spectra Classification Based on Multitask Residual Neural Network

At present, large-scale sky surveys have obtained a large volume of stellar spectra. Analyzing these spectra by manual methods cannot meet the actual needs, so it is necessary to explore automatic classification methods of stellar spectra. In this study, a multitask residual neural network is proposed, which adopts the idea of multitask learning to process two tasks simultaneously by using the correlation between the luminosity class and spectral subtype of stars. Moreover, the structure of residual neural network can reduce the parameters in the model to improve the calculation efficiency. We applied the multitask residual neural network to the classification of LAMOST DR4 M-type star spectra, and achieved good results in both luminosity class classification and spectral subtype classification. We further compared the performance of multitask residual neural network in stellar classification with that of random forest (RF) and eXtreme Gradient Boosting (XGBoost). The performance of multitask residual neural network is better than the other two algorithms.