Machine Learning Insights into Discrepancies Between Theoretical and Experimental Fission Barrier Heights

TL;DR

This paper employs machine learning, specifically XGBoost, to analyze and correct discrepancies between theoretical and experimental nuclear fission barrier heights, enhancing prediction accuracy and offering physical insights.

Contribution

It introduces a residual-learning machine learning framework that improves fission barrier predictions and interprets the physical factors influencing these barriers.

Findings

Machine learning reduces prediction errors to 0.3-1.2 MeV.

Inner barriers depend on binding energies and pairing effects.

Outer barriers are mainly influenced by proton number and Coulomb effects.

Abstract

Accurate determination of nuclear fission barrier heights is essential for understanding nuclear stability, fission dynamics, and nucleosynthesis. However, theoretical models such as the Extended Thomas-Fermi plus Strutinsky Integral (ETFSI) approach and the macroscopic-microscopic calculations of M\"oller et al. exhibit systematic deviations from experiment, especially in regions of strong deformation and pronounced shell effects. In this work, machine learning is used as a diagnostic tool to analyze these discrepancies. Using the Extreme Gradient Boosting (XGBoost) algorithm within a residual-learning framework, the model learns corrections to ETFSI predictions from physically motivated nuclear features, including proton and neutron numbers, binding energies, separation energies, and pairing-related quantities. The model reproduces experimental barrier heights with root-mean-squared errors of about 0.3-1.2 MeV across training, test, and cross-validation datasets. Feature-importance analysis shows that inner barriers depend on binding-energy trends, mass and neutron-number effects, and pairing contributions, whereas outer barriers are governed more strongly by macroscopic quantities, particularly proton number, consistent with the dominant role of Coulomb repulsion and fissility at large deformation. These results show that machine learning can improve predictive accuracy while providing physically interpretable insight into the limitations of theoretical nuclear models.