Bayesian Inference Based on Gaussian Process for Studying the α-Decay Half-Life of Atomic Nuclei

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Key Points

The aim is to develop a new analytic framework that quantifies parameter uncertainties in the alpha decay half-life using Bayesian inference and Gaussian processes.
Developed a new decay law formula integrating Bayesian inference and Gaussian processes.
Conducted sensitivity and feature importance analyses to identify key parameters affecting decay predictions.
Evaluated model performance using root mean square error (RMSE) on recent alpha decay data.
Achieved an overall RMSE of 0.375, with improved accuracy for even-even nuclei at 0.186.
Identified barrier penetration factor and nuclear mass number as the most significant contributors to decay predictions.
Model generalized well with recent observations, enhancing parameter uncertainty quantification.

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Bayesian inference models the alpha decay half-life in atomic nuclei, highlighting parameter uncertainties and systematic errors.

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Chen et al. (2026) studied this question.

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