基于Transformer特征提取的A型恒星光谱子型分类算法

Besvm: A-Type Star Spectral Subtype Classification Algorithm Based on Transformer Feature Extraction

恒星光谱分类是恒星光谱分析的重要工作之一。我国大型巡天项目LAMOST能够获得海量的恒星光谱数据,为了对海量恒星光谱数据进行高效分类,特别是对恒星光谱子型数据进行分类,需要研究快速有效的恒星光谱自动分类算法。提出一种基于Transformer特征提取的混合深度学习算法Bert+svm(简记为Besvm)实现A型恒星光谱子型的自动分类。该算法将A型恒星光谱26个线指数作为输入特征,应用Bert模型对26个线指数进行更深层次的学习,通过学习26个线指数的内在关联,进而提取到更有利于A型恒星光谱子型分类的特征。提取好的新特征被输入到分类器算法支持向量机(简记为SVM)中,进而对A型恒星光谱的三个子型A1、 A2和A3进行自动分类。此前,SVM算法在恒星光谱分类任务中已经有过应用,一些衍生的SVM算法在恒星光谱分类任务中也有较高的分类正确率。相比从前应用到恒星光谱分类任务的SVM算法,我们的混合深度学习算法受数据的信噪比影响较小,使用低信噪比数据也能有较高的分类正确率,并且所用数据量较少。通过五组实验验证了该算法的有效性和优越性:实验1用来对比选择优秀的核函数,通过光谱数据的匹配实验,最终选择了径向基核函数RBF;实验2对比了Besvm算法和其他四种传统优秀算法的性能指标,验证了Besvm算法的优越性;实验3用来检验Besvm算法的稳定性;实验4分析了数据量对Besvm算法的影响;实验5分析了不同信噪比数据对Besvm算法分类正确率的影响。综合实验结果分析表明,提出的混合深度学习算法Besvm在规模较小且信噪比低的数据集上仍能保持较高的分类正确率。Besvm总体分类错误率在0.01以下,远低于经典传统机器学习算法LDA算法,BP神经网络算法,SVM算法和Xgboost算法的分类错误率0.7, 0.66, 0.65, 0.36.需要说明的是BP神经网络算法的分类正确率过于受限于隐层神经元的个数。

Stellar spectrum classification is one of the important tasks of stellar spectrum analysis.Chinese large-scale survey project LAMOST can obtain massive stellar spectral data.In order to efficiently classify massive stellar spectral data,especially stellar spectral subtype data,we need to study fast and effective stellar spectral automatic classification algorithms.This paper proposes a hybrid deep learning algorithm based on Transformer feature extraction,Bert+svm(abbreviated as Besvm),to classify the spectral subtypes of type A stars automatically.The algorithm takes 26 line indices of the spectrum of A-type stars as input features and uses the Bert model to perform a deeper learning of the 26 line indices.By learning the internal correlation of the 26 line indices,it extracts the spectrum more conducive to the A-type stars classification characteristics.The extracted new features are input into the classifier algorithm Support Vector Machine(SVM for short),and then the three subtypes A1,A2,and A3 of the A-type star spectrum are automatically classified.Previously,the SVM algorithm has been applied in the stellar spectrum classification task,and some derivative SVM algorithms also have a higher classification accuracy rate in the stellar spectrum classification task.Compared with the SVM algorithm previously applied to the stellar spectral classification task,our hybrid deep learning algorithm is less affected by the signal-to-noise ratio of the data,and the low-signal-to-noise ratio data can also have a higher classification accuracy.The amount of data used is relatively small.This paper verifies the effectiveness and superiority of the algorithm through five sets of experiments:Experiment 1 is used to compare and select excellent kernel functions,and through the matching experiment of spectral data,the radial basis kernel function RBF is finally selected;Experiment 2 compares the performance indicators of the Besvm algorithm with the other four traditional excellent algorithms verify the superiority of the Besvm algorithm;Experiment 3 is used to test the stability of the Besvm algorithm;Experiment 4 analyzes the influence of the amount of data on the Besvm algorithm;Experiment 5 analyzes the influence of different signal-to-noise ratios data on the classification accuracy of Besvm algorithm.The analysis of comprehensive experimental results shows that the hybrid deep learning algorithm Besvm proposed in this paper can still maintain a high classification accuracy rate on a small-scale data set with a low signal-to-noise ratio.The overall classification error rate of Besvm is below 0.01,which is much lower than the error rate of the classic traditional machine learning algorithm LDA algorithm,Bp neural network algorithm,SVM algorithm and Xgboost algorithm.The classification accuracy is too limited by the number of hidden neurons.