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Geant4 Simulation on Lunar Surface Water Content Inversion Using the Chang’E-7 Neutron and Gamma-ray Spectrometer
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Guanyu CHEN1, 2, Tao MA1, 2, Yongqiang ZHANG2, Yan ZHANG2, Yongyi HUANG2, Kefan WU1, 2
Chinese Journal of Space Science | 2026, 46(2) : 312 - 319
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Chinese Journal of Space Science | 2026, 46(2): 312-319
Research Article
Geant4 Simulation on Lunar Surface Water Content Inversion Using the Chang’E-7 Neutron and Gamma-ray Spectrometer
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Guanyu CHEN1, 2, Tao MA1, 2, Yongqiang ZHANG2, Yan ZHANG2, Yongyi HUANG2, Kefan WU1, 2
Affiliations
  • 1Purple Mountain Observatory, Chinese Academy of Sciences, Nanjing 210023
  • 2School of Astronomy and Space Science, University of Science and Technology of China, Hefei 230026
Published: 2026-03-15 doi: 10.11728/cjss2026.02.2025-0144
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The Chang’E-7 lunar mission, scheduled for launch in 2026, has the primary scientific objective of detecting water-ice deposits within the Permanently Shadowed Regions (PSRs) at the lunar south pole. Understanding the distribution and concentration of lunar water ice is crucial for both fundamental science and future In-situ Resource Utilization (ISRU). In this study, we developed a high-fidelity model of the Chang’E-7 Lunar Neutron and Gamma-ray Spectrometer (LNGS) payload using the Geant4 toolkit (Version 10.07.p02) and established a quantitative inversion relationship between lunar surface water content and epithermal neutron count rates. The LNGS model, constructed by importing a detailed CAD model into Geant4, was rigorously validated against neutron beam calibration experiments conducted at the China Spallation Neutron Source (CSNS) Back-n facility. The results are as follows. The detector model shows excellent agreement with experimental data across the 0.4 eV to 1000 eV energy range, with a relative error of less than 6%, confirming the accuracy of the mass modeling and simulation setup. LNGS exhibits significant capability in discriminating soils with varying water content, as evidenced by both simulation and ground-based validation experiments using layered soil and water samples. Within the water-ice content range of 0.01% to 20%, simulations of Galactic Cosmic Ray (GCR) bombardment and subsequent neutron transport show that the epithermal neutron (400~700 keV) count rate decreases significantly with increasing hydrogen abundance. This relationship follows a modified Lawrence model with an exceptional coefficient of determination (R2 = 0.9993). The slight parameter differences compared to the original Lawrence model are attributed to the different simulation tools, lunar regolith composition models, and distinct detector designs and energy responses between LNGS and the Lunar Prospector neutron spectrometer. This study provides a robust theoretical framework and a specific, validated inversion model for interpreting Chang’E-7 orbital neutron data, directly enabling the mapping of hydrogen concentrations from measured count rates. It establishes fundamental technical support for the development of in-situ resource utilization technologies on the Moon and paves the way for high-precision assessment of water ice resources in the lunar polar regions.

Geant4  /  Neutron and Gamma-ray spectrometer  /  Lunar polar regions  /  Water-ice content
Guanyu CHEN, Tao MA, Yongqiang ZHANG, Yan ZHANG, Yongyi HUANG, Kefan WU. Geant4 Simulation on Lunar Surface Water Content Inversion Using the Chang’E-7 Neutron and Gamma-ray Spectrometer[J]. Chinese Journal of Space Science, 2026 , 46 (2) : 312 -319 . DOI: 10.11728/cjss2026.02.2025-0144
Year 2026 volume 46 Issue 2
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Article Info
doi: 10.11728/cjss2026.02.2025-0144
  • Receive Date:2025-08-05
  • Online Date:2026-07-08
  • Published:2026-03-15
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  • Received:2025-08-05
  • Revised:2025-11-11
Affiliations
    1Purple Mountain Observatory, Chinese Academy of Sciences, Nanjing 210023
    2School of Astronomy and Space Science, University of Science and Technology of China, Hefei 230026
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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