Enhancing the sensitivity of next-generation x-ray imaging detectors with artificial intelligence and advanced event reconstruction algorithms
Abstract
Advanced algorithms incorporating artificial intelligence and machine learning (AI/ML) enhance the sensitivity of X-ray imaging detectors and the scientific capabilities of future X-ray missions. In orbit, current X-ray instruments are limited in their sensitivity by (1) the instrumental background, induced by cosmic rays which produce signals that can be confused for genuine, astrophysical X-rays, and (2) the ability to reconstruct the detected photon events, degrading both the quantum efficiency and energy resolution at the lowest energies, where much discovery space resides. We report on the development of prototype algorithms designed to operate on the raw frame-level data to provide improved identification of particle-induced background events and enhanced energy reconstruction. These algorithms consider the contextual information from all signals in a frame holistically, and are built upon physics-motivated models of charge diffusion and signal generation within the detector. Using high fidelity simulations, we show that following recent developments, prototype ML algorithms can achieve a reduction of the unrejected particle background by up to 68 per cent compared with traditional filtering methods when operated in an aggressive mode suitable for source detection in X-ray imaging surveys, or up to 40 per cent in a more conservative mode designed to prioritize accurate measurements of the spectrum. Moreover, we find that next-generation event reconstruction algorithms…
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