Automated Classification of Massive Spectra Based on Enhanced Multi-Scale Coded Convolutional Neural Network

Abstract

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has produced massive medium-resolution spectra. Data mining for special and rare stars in massive LAMOST spectra is of great significance. Feature extraction plays an important role in the process of automatic spectra classification. The proper classification network can extract most of the common spectral features with minimum noise and individual features. Such a network has better generalization capabilities and can extract sufficient features for classification. A variety of classification networks of one dimension and two dimensions are both designed and implemented systematically in this paper to verify whether spectra is easier to deal with in a 2D situation. The experimental results show that the fully connected neural network cannot extract enough features. Although convolutional neural network (CNN) with a strong feature extraction capability can quickly achieve satisfactory results on the training set, there is a tendency for overfitting. Signal-to-noise ratios also have effects on the network. To investigate the problems above, various techniques are tested and the enhanced multi-scale coded convolutional neural network (EMCCNN) is proposed and implemented, which can perform spectral denoising and feature extraction at different scales in a more efficient manner. In a specified search, eight known and one possible cataclysmic variables (CVs) in LAMOST MRS are identified by EMCCNN including four CVs, one dwarf nova and three novae. The result supplements the spectra of CVs. Furthermore, these spectra are the first medium-resolution spectra of CVs. The EMCCNN model can be easily extended to search for other rare stellar spectra.

Keywords

• data mining
• spectral classification
• convolutional neural network
• cataclysmic variables