Experiments in machine learning of alpha-decay half-lives

2019/10/27 by Paulo S. A. Freitas, J. W. Clark, Freitas, Paulo S. A. +1
Engineering · Physics and Astronomy · #FOS: Physical sciences #Nuclear Physics and Applications #Nuclear Theory (nucl-th) #Nuclear physics research studies #Nuclear reactor physics and engineering

paper · pdf · doi:10.48550/arxiv.1910.12345

openalex publication_date 2019/10/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Artificial neural networks are trained by a standard backpropagation learning algorithm with regularization to model and predict the systematics of -decay of heavy and superheavy nuclei. This approach to regression is implemented in two alternative modes: (i) construction of a statistical global model based solely on available experimental data for alpha-decay half-lives, and (ii) modeling of the \it residuals between the predictions of state-of-the-art phenomenological model (specifically, the effective liquid-drop model (ELDM)) and experiment. Analysis of the results provide insights on the strengths and limitations of this application of machine learning (ML) to exploration of the nuclear landscape in regions beyond the valley of stability.

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