EUCLID: A New Approach to Constrain Nuclear Data via Optimized Validation Experiments using Machine Learning

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 Nuclear Data) set out to first identify where compensating could be hiding our libraries, and then design validation experiments optimized reduce chosen of data. Adjustment will performed assess whether the new experimental data—spanning measurements from multiple responses—successfully reduced errors. specific target are 239Pu fission, inelastic scattering, elastic capture, nu-bar, prompt fission neutron spectrum (PFNS). A experiment has been designed, which at National Criticality Experiments Research Center (NCERC).