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 aim for the trained
network is not just detect flares but also be able to distinguish typical false signals
(e.g. maxima of RR Lyr stars) from real flares.