Spectra Recognition Model for O-type Stars Based on Data Augmentation - Yang, Wen-Yu; Wu, Ke-Fei; Luo, A-Li; Zou, Zhi-Qiang (Frontiers in Astronomy and Space Sciences, 2021)
It is an ongoing issue in astronomy to recognize and classify O-type spectra comprehensively. The neural network is a popular recognition model based on data. The number of O-stars collected in LAMOST is <1% of AFGK stars, and there are only 127 O-type stars in the data release seven version. Therefore, there are not enough O-type samples available for recognition models. As a result, the existing neural network models are not effective in identifying such rare star spectra. This paper proposed a novel spectra recognition model (called LCGAN model) to solve this problem with data augmentation, which is based on Locally Connected Generative Adversarial Network (LCGAN). The LCGAN introduced the locally connected convolution and two timescale update rule to generate O-type stars' spectra. In addition, the LCGAN model adopted residual and attention mechanisms to recognize O-type spectra. To evaluate the performance of proposed models, we conducted a comparative experiment using a stellar spectral data set, which consists of more than 40,000 spectra, collected by the large sky area multi-object fiber spectroscopic telescope (LAMOST). The experimental results showed that the LCGAN model could generate meaningful O-type spectra. In our validation data set, the recognition accuracy of the data enhanced recognition model can reach 93.67%, 8.66% higher than that of the non-data enhanced identification model, which lays a good foundation for further analysis of astronomical spectra.