Bayesian Inference for Extracting Barrier Distributions from Fusion Excitation Functions

Abstract

Barrier distributions encode rich information about the structure and dynamics of fusing nuclei, but extracting them from experimental fusion cross sections requires an estimate of the fusion excitation function's second derivative. In this work we approach the task of extracting barrier distributions with uncertainty estimates from sparse experimental measurements as a Bayesian inference problem. We introduce a method based on AutoBNN, an interpretable Bayesian machine learning framework, to provide a robust statistical approach for analyzing fusion excitation functions. Benchmarking against Gaussian process regression on simulated excitation functions that span a wide range of realistic experimental conditions, we find that AutoBNN more faithfully recovers the underlying barrier distribution and reports well-calibrated uncertainties. We then apply the AutoBNN method to four experimentally measured heavy-ion fusion reactions where it mitigates spurious above-barrier structure and constrains existing predictions. Alongside these results, we have developed a user-friendly software implementation of our method, facilitating its application to future heavy and light-ion fusion experiments.