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

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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.

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