Application of Machine Learning Algorithms to Identify Problematic Nuclear Data

Abstract

In this work, we aim to show that machine learning algorithms are promising tools for the identification of nuclear data that contribute to increased errors in transport simulations. We demonstrate this through an application of a machine learning algorithm (Random Forest) to the Whisper/MCNP6 criticality validation library to identify nuclear data that are associated with an increase of the bias (simulated-experimental $k_{eff}$) in the calculations. Specifically, the $k_{eff}$ sensitivity profiles (with respect to nuclear data) of ${ }^{233}U$ solution benchmarks are used to predict the bias, and SHapley Additive exPlanations (SHAP) are used to explain how the sensitivities are related to the predicted bias. The SHAP values can be interpreted as sensitivity coefficients of the machine learning model to the $k_{eff}$ sensitivities that are used to make predictions of bias. Using the SHAP values, we can identify specific subsets of nuclear data that have the highest probability of influencing bias. We demonstrate the utility of this method by showing how SHAP values were used to identify an inconsistency in the ${ }^{19}F$ inelastic scattering nuclear data. The methodology presented here is not limited to transport problems and can be applied to other simulations if there are experimental measurements to compare against, simulations of those experimental measurements, and the ability to calculate sensitivities of the model output with respect to the data inputs.

Keywords:

• Machine learning
• nuclear data
• criticality

Acknowledgments

The authors would like to thank D. Neudecker for her valuable review and feedback of this paper. This work was partly supported by the U.S. Department of Energy (DOE) Nuclear Criticality Safety Program and partly supported by the Advanced Scientific Computing Program, Physics and Engineering Nuclear Physics subprogram, funded and managed by the National Nuclear Security Administration (NNSA). Work at LANL was carried out under the auspices of the NNSA of the DOE under contract 89233218CNA000001.

Notes

^a MCNP® and Monte Carlo N-Particle are registered trademarks owned by Triad National Security, LLC, manager and operator of Los Alamos National Laboratory. Any third party use of such registered marks should be properly attributed to Triad National Security, LLC, including the use of the designation as appropriate. For the purposes of visual clarity, the registered trademark symbol is assumed for all references to MCNP within the remainder of this paper.

^b “International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook,” Organisation for Economic Co-operation and Development, Nuclear Energy Agency.