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

Abstract

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.

9 pages, 10 figures, accepted by MNRAS

References in corpus (10)

• Modules for Experiments in Stellar Astrophysics (MESA)
• Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
• Standing on the shoulders of Dwarfs: the Kepler asteroseismic LEGACY sample II - radii, masses, and ages
• Standing on the shoulders of Dwarfs: the $Kepler$ asteroseismic LEGACY sample I - oscillation mode parameters
• Impact on asteroseismic analyses of regular gaps in Kepler data
• The K2 Galactic Archaeology Program Data Release 1: Asteroseismic results from Campaign 1
• Deep Learning Classification in Asteroseismology
• Modelling Kepler Red Giants in Eclipsing Binaries:Calibrating the Mixing Length Parameter with Asteroseismology
• Asteroseismology of 36 \emph{Kepler} subgiants -- I. Oscillation frequencies, linewidths and amplitudes
• Asteroseismic analysis of two alpha-enhanced stars KIC 7976303 and KIC 8694723