Stellar Spectral Classification Based on Capsule Network |
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| Institution: | 1. School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650500;2. School of Software Engineering, Xiamen Institute of Software Technology, Xiamen 361000;3. Center for Astrophysics, Guangzhou University, Guangzhou 510006;4. Key Laboratory of the Structure and Evolution of Celestial Objects, Chinese Academy of Sciences, Kunming 650011;1. National Time Service Center, Chinese Academy of Sciences, Xi’an 710600;2. Key Laboratory of Time and Frequency Primary Standards, Chinese Academy of Sciences, Xi’an 710600;3. School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing 100049;1. Yunnan Astronomical Observatories, Chinese Academy of Sciences, Kunming 650011;2. School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing 100049;1. Beijing Institute of Spacecraft Environment Engineering, Beijing 100094;2. National Key Laboratory of Science and Technology on Reliability and Environmental Engineering, Beijing 100094;3. Department of Engineering Physics, Tsinghua University, Beijing 100084;4. Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing 100084;1. Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030;2. University of Chinese Academy of Sciences, Beijing 100049;1. Key Laboratory foe Dark Matter and Space Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, Nanjing 210033;2. University of Chinese Academy of Sciences, Beijing 100049;3. School of Astronomy and Space Science, University of Science and Technology of China, Hefei 230026 |
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| Abstract: | The rapid development of large-scale sky survey project has produced a large amount of stellar spectral data, which make the automatic classification of stellar spectral data a challenging task. In this paper, we have proposed a stellar spectral classification method based on a capsule network. At first, by using the one-dimensional convolutional network and short-time Fourier transform (STFT), the one-dimensional spectra of the F5, G5, and K5 types selected from the LAMOST Data Release 5 (DR5) are converted into the two-dimensional Fourier spectrum images. Then, the two-dimensional Fourier spectrum images are classified automatically by the capsule network. Because the capsule network can preserve the hierarchical pose relationships among the entities in the image, and it does not need any pooling layers, the experimental results show that the capsule network has a better classification performance, for the classifications of the F5, G5, and K5-type stellar spectra, its classification accuracy is superior to other classification methods. |
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| Keywords: | Stars: fundamental parameters methods: data analysis techniques: spectral analysis |
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