  Space-based X-ray detectors are subject to significant fluxes of charged
particles in orbit, notably energetic cosmic ray protons, contributing a
significant background. We develop novel machine learning algorithms to detect
charged particle events in next-generation X-ray CCDs and DEPFET detectors,
with initial studies focusing on the Athena Wide Field Imager (WFI) DEPFET
detector. We train and test a prototype convolutional neural network algorithm
and find that charged particle and X-ray events are identified with a high
degree of accuracy, exploiting correlations between pixels to improve
performance over existing event detection algorithms. 99 per cent of frames
containing a cosmic ray are identified and the neural network is able to
correctly identify up to 40 per cent of the cosmic rays that are missed by
current event classification criteria, showing potential to significantly
reduce the instrumental background, and unlock the full scientific potential of
future X-ray missions such as Athena, Lynx and AXIS.
