Quantitative mobile gamma-ray spectrometry through Bayesian inference
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917238100983808 |
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| author | Breitenmoser, David Stabilini, Alberto Kasprzak, Malgorzata Magdalena Mayer, Sabine |
| author_facet | Breitenmoser, David Stabilini, Alberto Kasprzak, Malgorzata Magdalena Mayer, Sabine |
| contents | Accurate quantitative mapping of gamma-ray sources is critical for applications ranging from radiological emergency response and environmental monitoring to nuclear security and deep space exploration. Here, we show that integrating high-fidelity, platform-dynamic Monte Carlo simulations and Bayesian inference with mobile gamma-ray spectrometry enables rapid and accurate quantification of distributed and point-like gamma-ray sources. Validated against laboratory and field assays, our framework quantifies natural and anthropogenic gamma-ray sources that conventional methods cannot resolve in $1\,$s with $\sim\!\!1\,\%$ error. The developed method marks a critical advance in quantitative gamma-ray sensing, enabling improved radiological situational awareness, enhanced terrestrial geophysical and geochemical mapping, as well as more robust constraints on radionuclide abundances on extraterrestrial bodies across the Solar System. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_18769 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantitative mobile gamma-ray spectrometry through Bayesian inference Breitenmoser, David Stabilini, Alberto Kasprzak, Malgorzata Magdalena Mayer, Sabine Instrumentation and Detectors Applied Physics Computational Physics Data Analysis, Statistics and Probability Geophysics Accurate quantitative mapping of gamma-ray sources is critical for applications ranging from radiological emergency response and environmental monitoring to nuclear security and deep space exploration. Here, we show that integrating high-fidelity, platform-dynamic Monte Carlo simulations and Bayesian inference with mobile gamma-ray spectrometry enables rapid and accurate quantification of distributed and point-like gamma-ray sources. Validated against laboratory and field assays, our framework quantifies natural and anthropogenic gamma-ray sources that conventional methods cannot resolve in $1\,$s with $\sim\!\!1\,\%$ error. The developed method marks a critical advance in quantitative gamma-ray sensing, enabling improved radiological situational awareness, enhanced terrestrial geophysical and geochemical mapping, as well as more robust constraints on radionuclide abundances on extraterrestrial bodies across the Solar System. |
| title | Quantitative mobile gamma-ray spectrometry through Bayesian inference |
| topic | Instrumentation and Detectors Applied Physics Computational Physics Data Analysis, Statistics and Probability Geophysics |
| url | https://arxiv.org/abs/2512.18769 |