Convolutional Neural Networks for Flare Identification in TESS 2-minute Data ("stella")

Previous methods of flare detection with both Kepler and TESS data have relied on light curve detrending and using outlier detection heuristics for identifying flare events. stella is a novel way to detect flares in TESS short cadence data using convolutional neural networks (CNNs). Any TESS short cadence light curve can be run through the CNN models provided, without any detrending. The models created by the team return a probability light curve (see example figure), with values between 0-1 if a given light curve event is a flare or not. It takes < 1 minute to predict flares on a single TESS sector light curve using these models. The CNN models were created with Google's machine learning API, Tensorflow. The team has created 100 trained ensembled models to use when predicting flares in other short cadence TESS light curves. Any single model can be used on its own, however the team recommends using at least 10 models and averaging the results. The models can be opened and explored using either Tensorflow, h5py, or any other software that can open HDF5 files.