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

• Published in: EPJ Web of conferences Vol. 284; p. 15006
• Main Authors: Hutchinson, J., Alwin, J., Clark, A.R., Cutler, T., Grosskopf, M.J., Haeck, W., Herman, M.W., Kleedtke, N., Lamproe, J., Little, R.C., Michaud, I.J., Neudecker, D., Rising, M.E., Smith, T., Thompson, N., Vander Wiel, S., Wynne, N.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Les Ulis EDP Sciences 2023
• Subjects: Design optimization; Elastic scattering; Errors; Inelastic scattering; Machine learning; Research facilities

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 l...