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

Introduction

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.

Data Description

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

Contents

This record includes:

• *.csv: Output raw files from GEANT4, including 5D information, scattering angle, detector plate position, and particle type.
• backscatter_eventselection.py: Python code to filter events and generate a CSV file of selected backscattered events.
• *.tiff: Visualization files depicting Lunar and Martian scenarios, including detector geometry and particle events.
• ml_classifier.py: Python code for machine learning tasks to classify backscattered events.
• OP_Muographers_2023.pdf: Detailed description of chemical composition and simulated scenarios.
• Tracking_EKF: Performs track reconstruction and computes track lengths using extended Kalman Filter.

Disclaimer

The provided datasets are simulated samples suitable for conceptual R&D and performance studies. They have not been calibrated against real data and should not be used for physics projections about the detectors.