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.
The Science Products Module (SPM), a US contribution to the Athena Wide Field Imager, is a highly capable secondary CPU that performs special processing on the science data stream. The SPM will have access to both accepted X-ray events and those that were rejected by the on-board event recognition processing. It will include two software modules. The Transient Analysis Module will perform on-board processing of the science images to identify and characterize variability of the prime target and/or detection of serendipitous transient X-ray sources in the field of view. The Background Analysis Module will perform more sophisticated flagging of potential background events as well as improved background characterization, making use of data that are not telemetered to the ground, to provide improved background maps and spectra. We present the preliminary design of the SPM hardware as well as a brief overview of the software algorithms under development.
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