  The sensitivity of astronomical X-ray detectors is limited by the
instrumental background. The background is especially important when observing
low surface brightness sources that are critical for many of the science cases
targeted by future X-ray observatories, including Athena and future US-led
flagship or probe-class X-ray missions. Above 2keV, the background is dominated
by signals induced by cosmic rays interacting with the spacecraft and detector.
We develop novel machine learning algorithms to identify events in
next-generation X-ray imaging detectors and to predict the probability that an
event is induced by a cosmic ray vs. an astrophysical X-ray photon, enabling
enhanced filtering of the cosmic ray-induced background. We find that by
learning the typical correlations between the secondary events that arise from
a single primary, machine learning algorithms are able to successfully identify
cosmic ray-induced background events that are missed by traditional filtering
methods employed on current-generation X-ray missions, reducing the unrejected
background by as much as 30 per cent.
