https://doi.org/10.1051/epjconf/202430601001
Barrier distribution extraction via Gaussian process regression
Facility for Rare Isotope Beams, Michigan State University, East Lansing, Michigan 48824, USA
* e-mail: godbey@frib.msu.edu
Published online: 18 October 2024
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.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
