Signal Calibration and Energy Resolution Optimization of a Double-Sided Silicon Strip Detector for Lunar-Based Particle Detection

Abstract

This letter presents a signal calibration and energy resolution analysis of a double-sided silicon strip detector (DSSD) developed for charged particle detection in a lunar-based space environment. The detector is part of the Moon-Aiming Thai-Chinese Hodoscope, i.e., a proposed scientific payload for the Chang'E-7 lunar orbiter, aimed at monitoring space weather and lunar-surface particle interactions. To evaluate the DSSD's performance under vacuum conditions, alpha sources (Am-241 and Pu-239) were used to generate energy spectra, which were processed through baseline correction and histogram generation. Four peak models, i.e., Gaussian, Gaussian + Exponential Tail, exponentially modified Gaussian (EMG), and Hyper-EMG, were compared using nonlinear least squares. Results show that the Hyper-EMG model yields superior fits, especially for Am-241, achieving an average reduced chi-squared of 1.64 ± 4.44 and energy resolution of 3.09% ± 0.45%, with 22 out of 32 Akaike Information Criterion (AIC) wins. In contrast, Gaussian fits showed higher fitting errors (e.g., x2/DoF up to 10.5) and the poorest resolution. AIC selection further confirms Hyper-EMG's robustness, while Gaussian fits were consistently inadequate. These findings support the use of tail-aware models, such as Hyper-EMG, for accurate energy reconstruction in spaceborne silicon detectors.