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Inverting a new hydrogen map on the moon derived from Chang'E-2 gamma-ray spectrum data.

Yonghui Li1, Jiankun Zhao2, Feiliang Wang3

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Summary

This study uses machine learning to map lunar water by analyzing gamma-ray data. It identifies high hydrogen concentrations in specific regions, aiding future water exploration on the Moon.

Keywords:
(2)H characteristic γ rayCE2-GRSLM-BP neural networkLunar water

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Area of Science:

  • Planetary Science
  • Geophysics
  • Astrophysics

Background:

  • Extracting deuterium (2H) gamma-ray signals for lunar water detection is challenging due to interference from radioactive nuclides.
  • Traditional methods struggle to isolate the subtle 2H@2.223 MeV count, hindering accurate lunar water assessment.

Purpose of the Study:

  • To develop a novel method for accurately retrieving the 2H@2.223 MeV count from Chang'e-2 Gamma-Ray Spectrometer (CE2-GRS) data.
  • To create a global map of 2H@2.223 MeV counts to infer lunar water distribution.
  • To investigate the relationship between hydrogen abundance and lunar geological features.

Main Methods:

  • Integration of the Levenberg-Marquardt-Back Propagation (LM-BP) algorithm with three Back Propagation (BP) neural network models.
  • Training neural networks to process CE2-GRS data and extract the characteristic 2H@2.223 MeV gamma-ray counts.
  • Developing a machine learning approach to overcome interference from other radioactive nuclides.

Main Results:

  • A new global map of 2H@2.223 MeV counts was generated, revealing distinct regional variations.
  • Pronounced high counts (above 12 per 3s) were observed in the South Pole-Aitken Basin, Mare Imbrium, and Oceanus Procellarum.
  • Low counts (below 3 per 3s) were characteristic of lunar highland regions, indicating lower hydrogen abundance.

Conclusions:

  • Machine learning, specifically the LM-BP integrated neural network models, provides an effective solution for extracting subtle 2H signals and mapping lunar water.
  • Observed hydrogen distribution is linked to diverse geological activities and potentially influenced by factors like dry ice in permanently shadowed regions.
  • The study suggests a higher probability of water presence in the De Gerlache crater, warranting further investigation.