Development and Evaluation of a CNN-Based Charged-Particle Event Rejection Algorithm for Soft X-ray Detection in a pnCCD-Based Satellite System

Abstract

All-sky surveys in the soft X-ray band are essential for detecting transient objects such as high-redshift gamma-ray bursts (GRBs), which provide key insights into the early universe. HiZ-GUNDAM is a future satellite mission designed to detect and localize high-redshift GRBs. Its wide-field X-ray monitor, EAGLE, combines Lobster Eye Optics with a pnCCD imaging detector operating in the 0.4-4 keV band. Because of limited satellite telemetry, full-frame pnCCD images cannot be downlinked, requiring onboard event selection. Charged particles in the space environment produce background events that can be misidentified as X-ray photons, degrading detection sensitivity and potentially triggering false alerts. In this study, we developed a pnCCD readout system and evaluated charged-particle rejection using conventional grade methods and a convolutional neural network (CNN). Performance was evaluated using X-ray events from an Fe-55 source and electron events from a Sr-90 beta source. The CNN reduced the misclassification rate from 10.2-11.9% for conventional grade methods to 3.1% while maintaining a high acceptance rate for X-ray events. The improvement is particularly pronounced at higher deposited energies, reflecting the CNN's ability to distinguish track-like particle events from X-ray events by capturing detailed spatial features of charge distributions. Comparison with a thin-depletion-layer CMOS sensor further indicates that the thicker depletion layer of the pnCCD enhances discrimination performance. These results demonstrate that CNN-based event classification can substantially reduce charged-particle contamination while maintaining high X-ray acceptance, making it a promising approach for onboard event selection in future pnCCD-based wide-field X-ray missions.