Nuclear Data Evaluation Utilizing Bayesian Monte Carlo Methods

Abstract

While the body of experimental nuclear data continues to grow at a rapid pace, significant gaps persist. The lack of evaluated experimental data is far more pronounced in charged-particle reactions which are essential to a large portion of the isotope production community for vital medical, industrial, and national security interests. In the absence of experimental and evaluated data, isotope production relies on theoretical models that may result in low yield and isotopic impurities. This work investigates model and parameter adjustments in TALYS 2.0 to establish uncertainty bands on proton-induced cross sections, highlight influential parameters in calculations, and constrain adjustments within physically defensible bounds. The EXFOR dataset for proton-induced reactions on natural copper, comprised of 24 independent cross sections, was used for iterative Bayesian model updating. Upon convergence, parameters and their uncertainties were compared against 23 cumulative cross sections from EXFOR, highlighting the limitations of generalization. Various machine learning models were leveraged to predict TALYS performance, with a small neural network showing useful ranking capabilities across several generations, suggesting the use of a surrogate approach to screening for future evaluations.