Stellar classification with convolutional neural networks and photometric images: a new catalogue of 50 million SDSS stars without spectra

Abstract

ABSTRACT Stellar classification is a central topic in astronomical research that relies mostly on the use of spectra. However, with the development of large sky surveys, spectra are becoming increasingly scarce compared to photometric images. Numerous observed stars lack spectral types. In Sloan Digital Sky Survey (SDSS), there are more than hundreds of millions of such stars. In this paper, we propose a convolutional neural network-based stellar classification network (SCNet) in an attempt to solve the stellar classification task from photometric images alone, distinguishing between seven classes, i.e. O, B, A, F, G, K, and M. A total of 46 245 identified stellar objects were collected from the SDSS as the training samples for our network. Compared to many typical classification networks in deep learning, SCNet achieves the best classification accuracy of 0.861. When we allow an error to be within three neighbouring subtypes for SCNet, the accuracy even reaches 0.907. We apply the final SCNet model to 50 245 638 SDSS stars without corresponding spectra and present a new star classification catalogue, containing 7438 O-type stars, 31 433 B-type stars, 201 189 A-type stars, 910 007 F-type stars, 10 986 055 G-type stars, 18 941 155 K-type stars, and 19 168 361 M-type stars.