A Stellar Spectrum Classification Algorithm Based on CNN and LSTM Composite Deep Learning Model

Stellar spectral classification is a significant research direction in astronomy. With the rapid development of technology, the stellar spectral data collected by large survey telescopes have reached terabytes or even petabytes, and the traditional classification methods can no longer meet the processing needs of such a vast amount of data. CNNs learn the local features of the data by convolution operations, remove redundant information, and compress the features by maximum pooling operations. However, since the fully-connected layer of the original CNN model lacks the function of long-range dependency mining, this problem can be solved by adding LSTM networks, which can extract important features and detect small differences in features through their unique three gates of long-range dependency mining capability. Therefore, this paper proposes a deep model based on the composite of CNN and LSTM for classifying stellar spectra in LAMOST DR8. This model can better learn the features of stellar spectra, which provides an important help for stellar evolution studies. To improve the convergence speed of the model, the common Z-Score normalization method is used to process the data. The model proposed in this paper achieved a classification accuracy of 94.56% in the F, G, and K classification experiments. Meanwhile, compared with the previously used RBM, PILDNN, PILDNN*, DBN, Inception v3, 1D-SSCNN, and LSTM methods, the results show that the method in this paper has a higher classification accuracy. In the ten-class experiments, the method in this paper achieves 97.35% accuracy. The results are better than the experimental results using only LSTM and 1D-SSCNN methods, and the training time is reduced by nearly ten times. The F1 score is used to illustrate the classification accuracy of each class of stellar spectra, and the F1 value of each type is above 0.9 in both the three-classification and ten-class experiments. Compared with the results of previous experiments in the literature, the results of this paper's model are better. With the confusion matrix results, it can be concluded that the model's accuracy in this paper is higher in the experiments with more spectral categories, and it can even reach 100%. In summary, the model based on the combination of CNN and LSTM proposed in this paper can effectively classify large-scale stellar spectral data and achieve excellent classification results.