Generalization of Convolutional Neural Networks for Searching for O-Star Spectra Using Generative Adversarial Networks. (October 2020)

• Record Type: Journal Article
• Authors: Zheng, Zipeng; Qiu, Bo
• Is Part Of: Journal of physics. Volume 1626(2020)
• Publication Date: 2020-10
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1626/1/012017
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: In the stellar spectral data released by LAMSOT, the O-star spectrum is very rare, and the total amount of O-star spectra that can be utilized is only 156. We recommend generating a simulated real spectrum to overcome the above limitations. Using the real O-star spectrum as the model spectral image, we propose a one-dimensional spectral generation confrontation network (1D SGAN) to create artificial spectra based on real data sets. We use a combination of real and artificial spectra to train a one-dimensional convolutional neural network (1D CNN) to create a classifier that classifies the stellar spectra into seven categories. We demonstrate that using the proposed balanced data set with 1D SGAN generated images improves the performance of the 1D CNN classifier compared to the same 1D CNN trained with only the original data set.