Predicting Beta Decay Energy with Machine Learning

TL;DR

This paper demonstrates that ensemble machine learning models can accurately predict beta decay energies, with explainability tools revealing key nuclear features and physics-informed features enhancing model robustness.

Contribution

It introduces an ensemble ML approach with explainability for beta decay energy prediction, surpassing previous accuracy and integrating physics-based features.

Findings

Ensemble models outperform existing methods in $Q_\beta$ prediction.

Uncertainty and atomic number are key predictors.

Physics-informed features improve model robustness.

Abstract

$Q_\beta$ represents one of the most important factors characterizing unstable nuclei, as it can lead to a better understanding of nuclei behavior and the origin of heavy atoms. Recently, machine learning methods have been shown to be a powerful tool to increase accuracy in the prediction of diverse atomic properties such as energies, atomic charges, volumes, among others. Nonetheless, these methods are often used as a black box not allowing unraveling insights into the phenomena under analysis. Here, the state-of-the-art precision of the $\beta$-decay energy on experimental data is outperformed by means of an ensemble of machine-learning models. The explainability tools implemented to eliminate the black box concern allowed to identify uncertainty and atomic number as the most relevant characteristics to predict $Q_\beta$ energies. Furthermore, physics-informed feature addition improved models' robustness and raised vital characteristics of theoretical models of the nuclear structure.