Mode Angular Degree Identification in Subgiant Stars with Convolutional Neural Networks based on Power Spectrum

Minghao Du, Shaolan Bi, Xianfei Zhang, Yaguang Li, Tanda Li, Ruijie Shi

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.

https://arxiv.org/abs/2012.13120