Finding flares in Kepler and TESS data with recurrent deep neural networks

Abstract

Stellar flares are an important aspect of magnetic activity -- both for stellar evolution and circumstellar habitability viewpoints - but automatically and accurately finding them is still a challenge to researchers in the Big Data era of astronomy. We present an experiment to detect flares in space-borne photometric data using deep neural networks. Using a set of artificial data and real photometric data we trained a set of neural networks, and found that the best performing architectures were the recurrent neural networks (RNNs) using Long Short-Term Memory (LSTM) layers. The best trained network detected flares over {$5\sigma$ with $\gtrsim 80$\% recall and precision} and was also capable to distinguish typical false signals (e.g. maxima of RR Lyr stars) from real flares. Testing the network trained on Kepler data on TESS light curves showed that the neural net is able to generalize and find flares - with similar effectiveness - in completely new data having previously unseen sampling and characteristics.

9 pages (+1 appendix), 10 figures, accepted for publication at A&A

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• The NumPy array: a structure for efficient numerical computation
• The Transiting Exoplanet Survey Satellite
• The Kepler Catalog of Stellar Flares
• A path towards understanding the rotation-activity relation of M dwarfs with K2 mission, X-ray and UV data
• Optical flares from the faint mid-dM star 2MASS J00453911+4140396

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