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Traditional image segmentation methods employed with X-ray imaging detectors aboard X-ray space telescopes consist of two stages: first, a low energy threshold is applied; groups of activated pixels are then classified according to their shapes and identified as valid X-ray events or rejected as being possibly induced by cosmic rays. This method is fast and removes up to 98% of the cosmic ray-induced background. However, these traditional methods fail to address two important problems: first, they struggle to recover the true energies of, and sometimes fail to detect entirely, low-energy photons (photon energies less than 0.5keV); second, they consider only the shape of the active pixel regions, ignoring the longer-range context within the image frames. This limits their sensitivity to a specific type of cosmic ray signal: ”islands” created by secondary particles produced by cosmic rays hitting the body of the telescope (the shapes of which are often indistinguishable from X-ray photon signals). Together, these limitations hinder investigations of faint, diffuse targets, such as the outskirts of galaxies and galaxy clusters, and of ”low energy” targets such as individual stars, galaxies and high redshift systems. Both limitations can, however, be addressed with machine learning (ML) models. This work is part of our effort to develop fast and efficient background reduction methods for future astronomical X-ray missions using ML methods. We highlight several significant improvements in the classification and semantic segmentation of our background filtering pipeline. Our more realistic training and test data now incorporate the effects of readout noise and charge diffusion. In the presence of charge diffusion, our model is able to obtain an 80% relative improvement in lost signal recovery compared to the traditional background reduction techniques. We identify several directions for further development of the model.
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