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Stellar flares are an important aspect of magnetic activity -- both forstellar evolution and circumstellar habitability viewpoints - but automaticallyand accurately finding them is still a challenge to researchers in the Big Dataera of astronomy. We present an experiment to detect flares in space-bornephotometric data using deep neural networks. Using a set of artificial data andreal photometric data we trained a set of neural networks, and found that thebest performing architectures were the recurrent neural networks (RNNs) usingLong Short-Term Memory (LSTM) layers. The best trained network detected flaresover {$5\sigma$ with $\gtrsim80$\% recall and precision} and was also capableto distinguish typical false signals (e.g. maxima of RR Lyr stars) from realflares. Testing the network trained on Kepler data on TESS light curves showedthat the neural net is able to generalize and find flares - with similareffectiveness - in completely new data having previously unseen sampling andcharacteristics.

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