  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.
