Abstract
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.
Highlights
An avalanche of astronomical data is expected with the completion of state-of-the-art survey telescopes, such as the Sloan Digital Sky Survey (SDSS) and Large Sky Area Multiobject Fiber Spectroscopy Telescope (LAMOST)
This work aimed to improve the topological structure of traditional convolutional neural network (CNN), and an ensemble convolutional neural network (ECNN) model was proposed and applied to improve the classification accuracy
Discussion and Conclusions is work focused on the extraction of the convolution features of stellar spectra through CNN and evaluated the validity of its analysis in stellar spectrum classification
Summary
An avalanche of astronomical data is expected with the completion of state-of-the-art survey telescopes, such as the Sloan Digital Sky Survey (SDSS) and Large Sky Area Multiobject Fiber Spectroscopy Telescope (LAMOST). SDSS and LAMOST surveys have produced massive amounts of spectral data. Hon et al [11] achieved excellent results in spectral classification through 1D convolutional neural network (CNN). Ese methods often fail to achieve ideal results because of the low SNR spectra. To address these problems, this work aimed to improve the topological structure of traditional convolutional neural network (CNN), and an ensemble convolutional neural network (ECNN) model was proposed and applied to improve the classification accuracy. Ensemble learning [14,15,16,17,18,19] is a machine learning method that combines multiple individual learners to obtain a better result. By building multiple basic classifiers and gently integrates each learning result according to certain integration strategies, the generalization ability of the model was improved and obtains better results than a single basic classifier
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