Identifying the angular degrees l of oscillation modes is essential for asteroseismology and depends on visual tagging before fitting power spectra in a so-called peakbagging analysis. In oscillating subgiants, radial (l= 0) mode frequencies distributed linearly in frequency, while non-radial (l >= 1) modes are p-g mixed modes that having a complex distribution in frequency, which increased the difficulty of identifying l. In this study, we trained a 1D convolutional neural network to perform this task using smoothed oscillation spectra. By training simulation data and fine-tuning the pre-trained network, we achieved a 95 per cent accuracy on Kepler data.
@article{arxiv.2012.13120,
title = {Mode Angular Degree Identification in Subgiant Stars with Convolutional Neural Networks based on Power Spectrum},
author = {Minghao Du and Shaolan Bi and Xianfei Zhang and Yaguang Li and Tanda Li and Ruijie Shi},
journal= {arXiv preprint arXiv:2012.13120},
year = {2021}
}