Mode angular degree identification in subgiant stars with convolutional neural networks based on power spectrum. Issue 1 (22nd December 2020)

• Record Type: Journal Article
• Authors: Du, Minghao; Bi, Shaolan; Zhang, Xianfei; Li, Yaguang; Li, Tanda; Shi, Ruijie
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 501:Issue 1(2021)
• Page Start: 614
• Page End: 622
• Publication Date: 2020-12-22
• Subjects: asteroseismology -- methods: data analysis -- techniques: image processing -- stars: statistics; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/staa3642
• Languages: English
• ISSNs: 0035-8711

Abstract

ABSTRACT: The identification of the angular degrees l of oscillation modes is essential for asteroseismology and it depends on visual tagging before fitting power spectra in a so-called peakbagging analysis. In oscillating subgiants, radial ( l = 0) mode frequencies are distributed linearly in frequency, while non-radial ( l ≥ 1) modes are p–g mixed modes that have a complex distribution in frequency that increases the difficulty of identifying l . In this study, we trained a one-dimensional convolutional neural network to perform this task using smoothed oscillation spectra. By training simulation data and fine-tuning the pre-trained network, we achieved 95 per cent accuracy for Kepler data.