Bayesian Inference for Extracting Barrier Distributions from Fusion Excitation Functions
This paper introduces a Bayesian inference framework using AutoBNN to robustly extract barrier distributions with well-calibrated uncertainties from sparse fusion excitation function data, outperforming Gaussian process regression in benchmarks and successfully mitigating spurious structures in experimental heavy-ion fusion reactions.