Barrier Distribution Extraction Via Gaussian Process Regression

This work presents a novel method for extracting potential barrierdistributions from experimental fusion cross sections. We utilize a simpleGaussian process regression (GPR) framework to model the observed crosssections as a function of energy for three nuclear systems. The GPR approachoffers a flexible way to represent the experimental data, accommodatingpotentially complex behavior without introducing strong prior assumptions. Thismethod is applied directly to experimental data and is compared to thetraditional direct extraction technique. We discuss the advantages of GPR-basedbarrier distribution extraction, including the capability to quantifyuncertainties and robustness to noise in the experimental data.