Finding flares in <i>Kepler</i> and TESS data with recurrent deep neural networks
Krisztián Vida, Attila Bódi, Tamás Szklenár, Bálint Seli
Abstract
Stellar flares are an important aspect of magnetic activity – from both 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 using long short-term memory layers. The best trained network detected flares over 5 σ with ≳80% recall and precision and was also capable of distinguishing 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 with different sampling and characteristics from those of the training set ő.