Finding Flares In Kepler And Tess Data With Recurrent Deep Neural Networks

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 sigma with greater than or similar to 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 o.