Speaker
Description
Nuclear level densities (NLD) and gamma strength functions (gSF) are key parameters used to calculate neutron-capture cross sections where experimental data does not exist. Current Hauser-Feshbach calculations allow for the use of a variety of models for NLD and gSF, ranging from phenomenological to microscopic. Using published experimental data, as well as Hauser-Feshbach calculations performed using TALYS [1], a machine learning model is being developed to improve NLD and gSF model predictions. The Molybdenum isotopic chain was chosen as an initial use case for this development because of the large number of stable isotopes and the amount of available information on statistical properties from previous work. The developed model will be validated against new results from nuclear resonance fluorescence and particle evaporation measurements. Additionally, we plan to test the model using results from upcoming beta-Oslo experiments to demonstrate its effectiveness as we move away from stability. The goal of this work is to improve parameter choices in Hauser-Feshbach calculations and strengthen the foundation for accurate nuclear data evaluations critical to a variety of applications.
Acknowledgement: This work was supported by the Office of Defense Nuclear Nonproliferation Research and Development within the U.S. Department of Energy’s National Nuclear Security Administration.
References:
[1] Koning, A., Hilaire, S., and Goriely, S., (2023) Eur. Phys. J. A 59, 131