SFNet:Stellar Feature Network with CWT for Stellar Spectra Recognition

作     者：Hao Fu Peng Liu Xuan Qi Xue Mei 

作者机构：College of Electrical Engineering and Control Science Nanjing Tech University 

出 版 物：《Research in Astronomy and Astrophysics》 (天文和天体物理学研究)

年 卷 期：2024年第9期

页      面：262-272页

核心收录：

学科分类：07[理学] 070401[理学-天体物理] 0704[理学-天文学] 

摘      要：Stellar spectral classification is crucial in astronomical data analysis. However, existing studies are often limited by the uneven distribution of stellar samples, posing challenges in practical applications. Even when balancing stellar categories and their numbers, there is room for improvement in classification accuracy. This study introduces a Continuous Wavelet Transform using the Super Morlet wavelet to convert stellar spectra into wavelet images. A novel neural network, the Stellar Feature Network, is proposed for classifying these images. Stellar spectra from Large Sky Area Multi-Object Fiber Spectroscopic Telescope DR9, encompassing five equal categories (B, A, F, G,K), were used. Comparative experiments validate the effectiveness of the proposed methods and network,achieving significant improvements in classification accuracy.