Detecting Lunar and Martian Water via Backscattered Cosmic Particles using Muon Tomography

<strong>Introduction</strong> The search for water on the Lunar and Martian surfaces is a cornerstone of space exploration, playing a key role in expanding our understanding of the history and evolution of these celestial bodies. Despite its importance, current knowledge about the distribution, concentration, origin, and migration of water on the Moon and Mars is still limited. This study aims to address these gaps by employing a novel approach that leverages cosmic-ray muon detectors and backscattered radiation. Through the use of advanced muon tracking systems and preliminary simulations conducted with GEANT4, the research suggests that muon tomography holds significant promise for improving our understanding of water-ice content on the Lunar and Martian surfaces. <strong>Data Description</strong> Data and detector models were generated using GEANT4. The simulations include: Lunar and Martian dry regolith Lunar and Martian regolith with water-ice beneath the surface <strong>Contents</strong> This record includes: <code>*.csv</code>: Output raw files from GEANT4, including 5D information, scattering angle, detector plate position, and particle type. <code>backscatter_eventselection.py</code>: Python code to filter events and generate a CSV file of selected backscattered events. <code>*.tiff</code>: Visualization files depicting Lunar and Martian scenarios, including detector geometry and particle events. <code>ml_classifier.py</code>: Python code for machine learning tasks to classify backscattered events. <code>OP_Muographers_2023.pdf</code>: Detailed description of chemical composition and simulated scenarios. Tracking_EKF: Performs track reconstruction and computes track lengths using extended Kalman Filter. <strong>Disclaimer</strong> The provided datasets are simulated samples suitable for conceptual R&amp;D and performance studies. They have not been calibrated against real data and should not be used for physics projections about the detectors.<strong>引言</strong> 探索月球与火星表面的水是太空探索的核心任务之一，对于深化我们对这两个天体的历史与演化的认知具有关键作用。尽管其重要性不言而喻，但目前人类对月球与火星上水的分布、浓度、起源及运移机制的认知仍较为有限。本研究旨在通过一种利用宇宙射线μ子探测器（cosmic-ray muon detectors）与背散射辐射的创新方法，填补上述研究空白。借助先进的μ子跟踪系统与基于GEANT4开展的初步模拟，本研究表明μ子层析成像技术对于深化我们对月球与火星表面水冰含量的认知具有可观的应用前景。<strong>数据说明</strong> 数据与探测器模型均通过GEANT4生成。本次模拟涵盖两类场景：月球与火星的干燥风化层，以及表层下方含有水冰的月球与火星风化层。<strong>数据集内容</strong> 本数据集包含以下内容：<code>*.csv</code>：GEANT4输出的原始数据文件，包含五维信息、散射角、探测器平板位置以及粒子类型。<code>backscatter_eventselection.py</code>：用于筛选事件并生成选定背散射事件CSV文件的Python代码。<code>*.tiff</code>：用于可视化月球与火星场景的图像文件，涵盖探测器几何结构与粒子事件信息。<code>ml_classifier.py</code>：用于开展背散射事件分类机器学习任务的Python代码。<code>OP_Muographers_2023.pdf</code>：详细阐述化学成分与模拟场景的文档。Tracking_EKF：基于扩展卡尔曼滤波器（extended Kalman Filter）实现径迹重建并计算径迹长度的工具。<strong>免责声明</strong> 本数据集为模拟样本，仅适用于概念性研发与性能研究。尚未针对真实数据进行校准，不得用于探测器相关的物理投影分析。