Methodology for physics-informed generation of synthetic neutron time-of-flight measurement data

• Published in: arXiv.org 2023-09
• Main Authors: Walton, Noah, Brown, Jesse, Fritsch, William, Brown, Dave, Nobre, Gustavo, Sobes, Vladimir
• Format: Article
• Language: English
• Subjects: Datasets; Evaluation; Methodology; Nuclear cross sections; Parameter estimation; Process parameters
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2303.09698

Accurate neutron cross section data are a vital input to the simulation of nuclear systems for a wide range of applications from energy production to national security. The evaluation of experimental data is a key step in producing accurate cross sections. There is a widely recognized lack of reproducibility in the evaluation process due to its artisanal nature and therefore there is a call for improvement within the nuclear data community. This can be realized by automating/standardizing viable parts of the process, namely, parameter estimation by fitting theoretical models to experimental data. This automation effort could greatly benefit from a synthetic data resource. This work leverages problem-specific physics, Monte Carlo sampling, and a general methodology for data synthesis to generate unlimited, labelled experimental cross-section data that is statistically indistinguishable to the observed data. Heuristic and, where applicable, rigorous statistical comparisons to observed data support this claim. The demonstration is based on/limited to transmission measurements at Rensselaer Polytechnic Institute (RPI) and energy-differential cross sections in the resolved resonance region (RRR). An open-source software is published alongside this article that executes the complete methodology to produce high-utility synthetic datasets. The goal of this work is to provide an approach and corresponding tool that will allow the evaluation community to begin exploring more data-driven, ML-based solutions to long-standing challenges in the field.