Informing nuclear physics via machine learning methods with differential and integral experiments

Denise Neudecker, Oscar Cabellos, Alexander R. Clark, Michael J. Grosskopf, Wim Haeck, Michal W. Herman, Jesson Hutchinson, Toshihiko Kawano, Amy E. Lovell, Ionel Stetcu, Patrick Talou, and Scott Vander Wiel
Phys. Rev. C 104, 034611 – Published 10 September 2021

Abstract

Background: Information from differential nuclear-physics experiments and theory is often too uncertain to accurately define nuclear-physics observables such as cross sections or energy spectra. Integral experimental data, representing the applications of these observables, are often more precise but depend simultaneously on too many of them to unambiguously identify issues in the observable with human expert analysis alone.

Purpose: We explore how we can leverage physics knowledge gained from differential experimental data, nuclear theory, integral experiments, and neutron-transport calculations to better understand nuclear-physics observables in the context of the application area represented by integral experiments. We support this task with machine-learning methods to discern trends in a large amount of convoluted data.

Methods: Differential and integral information was used in an analysis augmented by the random forest and the Shapley additive explanations metric. We chose as an application area one that is represented by criticality measurements and pulsed-sphere neutron-leakage spectra.

Results: We show one representative example (Pu241 fission observables) where the combination of differential and integral information allowed to resolve issues in data representing these observables. As a starting point, the machine learning (ML) algorithms highlighted several observables as leading potentially to bias in simulating integral experiments. Differential information, paired with sensitivity to integral quantities, allowed us then to pinpoint one specific observable (Pu241 fission cross section) as the main driver of bias. The comparison to integral experiments, on the other hand, allowed us to indicate a likely reliable experiment among several discrepant ones for this observables. In other cases (e.g., Pu239 observables), we were not able to resolve the confounding introduced by integral experiments but instead highlighted the need for targeted new experiments and theory developments to better constrain the nuclear-physics space for the application area represented by integral experiments.

Conclusions: We were able to combine information from differential experimental data, nuclear-physics theory, integral experiments, and neutron-transport simulations of the latter experiments with the help of the random forest algorithm and expert judgment. This combination of knowledge allows to improve our description of nuclear-physics observables as applied to a particular application area.

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  • Received 15 April 2021
  • Accepted 23 August 2021

DOI:https://doi.org/10.1103/PhysRevC.104.034611

©2021 American Physical Society

Physics Subject Headings (PhySH)

Nuclear Physics

Authors & Affiliations

Denise Neudecker1,*, Oscar Cabellos2, Alexander R. Clark1, Michael J. Grosskopf1, Wim Haeck1, Michal W. Herman1, Jesson Hutchinson1, Toshihiko Kawano1, Amy E. Lovell1, Ionel Stetcu1, Patrick Talou1, and Scott Vander Wiel1

  • 1Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
  • 2Department of Energy Engineering and Instituto de Fusión Nuclear, Universidad Politécnica de Madrid, 28006 Madrid, Spain

  • *dneudecker@lanl.gov

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Vol. 104, Iss. 3 — September 2021

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