Automated Stellar Spectra Classification with Ensemble Convolutional Neural Network
Large sky survey telescopes have produced a tremendous amount of astronomical data, including spectra. Machine learning methods must be employed to automatically process the spectral data obtained by these telescopes. Classification of stellar spectra by applying deep learning is an important research direction for the automatic classification of high-dimensional celestial spectra. In this paper, a robust ensemble convolutional neural network (ECNN) was designed and applied to improve the classification accuracy of massive stellar spectra from the Sloan digital sky survey. We designed six classifiers which consist six different convolutional neural networks (CNN), respectively, to recognize the spectra in DR16. Then, according the cross-entropy testing error of the spectra at different signal-to-noise ratios, we integrate the results of different classifiers in an ensemble learning way to improve the effect of classification. The experimental result proved that our one-dimensional ECNN strategy could achieve 95.0% accuracy in the classification task of the stellar spectra, a level of accuracy that exceeds that of the classical principal component analysis and support vector machine model.
- Research Organization:
- Shandong University, Shandong (China)
- Sponsoring Organization:
- USDOE Office of Science (SC); Shandong Provincial Natural Science Foundation; Alfred P. Sloan Foundation; University of Utah
- Grant/Contract Number:
- ZR2020MA064
- OSTI ID:
- 1983018
- Journal Information:
- Advances in Astronomy, Vol. 2022; ISSN 1687-7969
- Publisher:
- HindawiCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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