Signal Calibration and Energy Resolution Optimization of a Double-Sided Silicon Strip Detector for Lunar-Based Particle Detection - Thanayuth Panyalert; Shariff Manuthasna; Peerapong Torteeka; Xu He; Ning Zhang; Jianing Zheng; Bin Zhang; Dong Yang; Haibo Yang; Jingtian Xian; Yiwei Bao; Sichen Lu; et al. (IEEE Sensors Letters, 2025)
This paper 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 (MATCH), 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 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 $\pm$ 4.44 and energy resolution of 3.09% $\pm$ 0.45%, with 22 out of 32 AIC wins. In contrast, Gaussian fits showed higher fitting errors (e.g., $\chi ^{2}$/DoF up to 10.5) and the poorest resolution. Akaike Information Criterion (AIC) selection further confirms Hyper-EMG's robustness, while Gaussian fits were consistently inadequate. These findings support the use of tail-aware models like Hyper-EMG for accurate energy reconstruction in spaceborne silicon detectors.
Keywords: Planetary Science and Exploration; Spacecraft Design and Technology; Particle Detector Development and Performance