Issue |
EPJ Web of Conf.
Volume 284, 2023
15th International Conference on Nuclear Data for Science and Technology (ND2022)
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Article Number | 16003 | |
Number of page(s) | 8 | |
Section | Computational Techniques and Machine Learning | |
DOI | https://doi.org/10.1051/epjconf/202328416003 | |
Published online | 26 May 2023 |
https://doi.org/10.1051/epjconf/202328416003
Uncovering Where Compensating Errors Could Hide in ENDF/B-VIII.0
Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
* e-mail: dneudecker@lanl.gov
Published online: 26 May 2023
Unconstrained physics spaces between two or more nuclear data observables in a library occur when their values can be simultaneously adjusted without violating the uncertainties in either differential information or simulations of relevant integral experiments. Differential data are often too imprecise to fully bound all nuclear data observables of interest for application simulations. Integral data are simulated with combinations of nuclear data so that an error in one observable may be hidden by a counterbalancing error in another. In this manner compensating errors may lurk within nuclear data libraries and these errors have the potential to undermine the predictive power of neutron transport simulations, particularly in situations where there is no conclusive validation experiment that resembles the application of interest. The EUCLID project (Experiments Underpinned by Computational Learning for Improvements in Nuclear Data) developed a preliminary workflow to identify these unconstrained physics spaces by bringing together results from a large collection of integral experiments with their simulated counter-parts as well as differential information that have a one-to-one correspondence to nuclear data. This wealth of information is processed by machine learning tools for subsequent refinement by human experts. Here, we show how the EUCLID work-flow is executed by applying it first to 239Pu and then to 9Be nuclear data in ENDF/B-VIII.0.
© 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.
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