Classification of star/galaxy/QSO and star spectral types from LAMOST data release 5 with machine learning approaches

Abstract

We use 343,747 sources from LAMOST DR5 to do star/galaxy/QSO classification with machine learning approaches. Specifically, the 312,767 spectral labeled stars (G, K, M, F, A) are used to do star classification. The photometry of u, g, r, i, z, J, and H are used as machine learning features. For star/galaxy/QSO classification, the k nearest neighbor algorithm (KNN), decision tree (DT), random forest (RF) and support vector machine (SVM) perform well. For star classification, the accuracy of RF and SVM classification are higher than the accuracy of KNN and DT. The area under receiver operating characteristic curves of the four models are approaching to 1. The accuracy, precision, recall, f_score, Matthews correlation coefficient are always greater than 0.5. The four models perform all right in predicting the nature of sources and the star label.