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September 27, 2021
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Informing nuclear physics via machine learning methods with differential and integral experiments

Publicated to: PHYSICAL REVIEW C. 104 (3): 34611- - 2021-09-10 104(3), DOI: 10.1103/PhysRevC.104.034611

Authors:

Neudecker, D; Cabellos, O; Clark, AR; Grosskopf, MJ; Haeck, W; Herman, MW; Hutchinson, J; Kawano, T; Lovell, AE; Stetcu, I; Talou, P; Vander Wiel, S
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Affiliations

Los Alamos Natl Lab, Los Alamos, NM 87545 USA - Author
Univ Politecn Madrid, Dept Energy Engn, E-28006 Madrid, Spain - Author
UNIV POLITECN MADRID, INST FUS NUCL, E-28006 MADRID, SPAIN - Author
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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 (Pu-241 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 (Pu-241 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., Pu-239 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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Keywords

Cross-sectionLibraryNeutron-induced fissionPu-241SpectraU-235UraniumYield

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal PHYSICAL REVIEW C due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2021, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category Nuclear and High Energy Physics.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 2.88. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 13, 2025)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 2.2 (source consulted: FECYT Mar 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2026-04-24, the following number of citations:

  • WoS: 24
  • Scopus: 27
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-24:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 10.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 10 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/92755/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 53
  • Downloads: 28
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Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: United States of America.

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Awards linked to the item

We thank K. J. Kelly, P. Koehler, and R. C. Little (all Los Alamos National Laboratory, LANL) for insightful comments and discussions. Work at LANL was carried out under the auspices of the National Nuclear Security Administration (NNSA) of the U.S. Department of Energy (DOE) under Contract No. 89233218CNA000001. Research reported in this publication was partially supported by the U.S. DOE Laboratory Directed Research & Development (LDRD) program at LANL. We gratefully acknowledge partial support of the Advanced Simulation and Computing program at LANL and the DOE Nuclear Criticality Safety Program (NCSP), funded and managed by NNSA for the DOE.
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