# Deep Learning Classification in Asteroseismology

## TL;DR

This paper develops a convolutional neural network to classify red giant stars' evolutionary stages from their oscillation spectra, achieving high accuracy and significantly expanding classified star datasets.

## Contribution

The study introduces a CNN-based method for automatic classification of red giant stars' evolutionary states using Kepler data, with high accuracy and broad applicability.

## Key findings

- Achieved up to 99% accuracy in classifying stellar evolutionary stages.
- Successfully classified 5379 previously unclassified Kepler red giants.
- Demonstrated CNN's capability to learn visual features from oscillation spectra.

## Abstract

In the power spectra of oscillating red giants, there are visually distinct features defining stars ascending the red giant branch from those that have commenced helium core burning. We train a one-dimensional convolutional neural network by supervised learning to automatically learn these visual features from images of folded oscillation spectra. By training and testing on \textit{Kepler} red giants, we achieve an accuracy of up to 99\% in separating helium-burning red giants from those ascending the red giant branch. The convolutional neural network additionally shows capability in accurately predicting the evolutionary states of 5379 previously unclassified \textit{Kepler} red giants, by which we now have greatly increased the number of classified stars.