Deep learning classification in asteroseismology using an improved neural network: results on 15 000 Kepler red giants and applications to K2 and TESS data
Abstract
Deep learning in the form of 1D convolutional neural networks have previously been shown to be capable of efficiently classifying the evolutionary state of oscillating red giants into red giant branch stars and helium-core burning stars by recognizing visual features in their asteroseismic frequency spectra. We elaborate further on the deep learning method by developing an improved convolutional neural network classifier. To make our method useful for current and future space missions such as K2, TESS, and PLATO, we train classifiers that are able to classify the evolutionary states of lower frequency resolution spectra expected from these missions. Additionally, we provide new classifications for 8633 Kepler red giants, out of which 426 have previously not been classified using asteroseismology. This brings the total to 14983 Kepler red giants classified with our new neural network. We also verify that our classifiers are remarkably robust to suboptimal data, including low signal-to-noise and incorrect training truth labels.
- Publication:
-
Monthly Notices of the Royal Astronomical Society
- Pub Date:
- May 2018
- DOI:
- 10.1093/mnras/sty483
- arXiv:
- arXiv:1802.07260
- Bibcode:
- 2018MNRAS.476.3233H
- Keywords:
-
- asteroseismology;
- methods: data analysis;
- stars: evolution;
- stars: oscillations;
- stars: statistics;
- Astrophysics - Instrumentation and Methods for Astrophysics;
- Astrophysics - Solar and Stellar Astrophysics
- E-Print:
- 12 pages, 14 figures, 4 tables. Accepted for publication in the Main Journal of MNRAS. The catalogue containing updated evolutionary state classifications of ~16000 Kepler red giants is available as an ancillary file