  This work presents a novel method for extracting potential barrier
distributions from experimental fusion cross sections. We utilize a simple
Gaussian process regression (GPR) framework to model the observed cross
sections as a function of energy for three nuclear systems. The GPR approach
offers a flexible way to represent the experimental data, accommodating
potentially complex behavior without introducing strong prior assumptions. This
method is applied directly to experimental data and is compared to the
traditional direct extraction technique. We discuss the advantages of GPR-based
barrier distribution extraction, including the capability to quantify
uncertainties and robustness to noise in the experimental data.
