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Signal Calibration and Energy Resolution Optimization of a Double-Sided Silicon Strip Detector for Lunar-Based Particle Detection
Author(s)
Panyalert, Thanayuth
Manuthasna, Shariff
He, Xu
Zhang, Ning
Zheng, Jianing
Zhang, Bin
Yang, Dong
Yang, Haibo
Xian, Jingtian
Bao, Yiwei
Lu, Sichen
Puprasit, Kunlanan
Chaiwongkhot, Kullapha
Marsri, Tanawish
Zhao, Haojiang
Pittayang, Yaowarat
Jamlongkul, Paparin
Charoenvicha, Popefa
Khonsri, Pakorn
Anuchit, Kanatip
Amratisha, Koth
Burom, Sunruthai
Lakronwat, Jidapa
Mitthumsiri, Warit
Pattarakijwanich, Petchara
Ruffolo, David
Zhang, Shenyi
Rujopakarn, Wiphu
Date Issued
January 1, 2025
Type
Article
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.
Citation
IEEE Sensors Letters, 9(7), 2025