https://doi.org/10.1051/epjconf/202328415006
EUCLID: A New Approach to Constrain Nuclear Data via Optimized Validation Experiments using Machine Learning
Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, NM, 87545, USA
* e-mail: jesson@lanl.gov
Published online: 26 May 2023
Compensating errors between several nuclear data observables in a library can adversely impact application simulations. The EUCLID project (Experiments Underpinned by Computational Learning for Improvements in Nuclear Data) set out to first identify where compensating errors could be hiding in our libraries, and then design validation experiments optimized to reduce compensating errors for a chosen set of nuclear data. Adjustment of nuclear data will be performed to assess whether the new experimental data—spanning measurements from multiple responses—successfully reduced compensating errors. The specific target nuclear data for EUCLID are 239Pu fission, inelastic scattering, elastic scattering, capture, nu-bar, and prompt fission neutron spectrum (PFNS). A new experiment has been designed, which will be performed at the National Criticality Experiments Research Center (NCERC).
© The Authors, published by EDP Sciences, 2023
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.
