Model uncertainty in accelerator application simulations

TL;DR

This paper assesses the uncertainties in nuclear reaction simulations used in accelerator physics, highlighting how practitioner expertise and code design influence the accuracy and reliability of model predictions based on experimental data.

Contribution

It introduces a methodology to evaluate model uncertainties considering practitioner expertise and code design, based on comprehensive benchmark studies and experimental data.

Findings

Experienced users' simulations align with benchmark limits.

Less experienced users tend to underestimate or overestimate uncertainties.

Model error ratios vary with user expertise and code design.

Abstract

Monte-Carlo nuclear reaction and transport codes are widely used to devise accelerator-based nuclear physics experiments; at the same time, many experiments are performed to validate the Monte-Carlo codes, which can be used for the design of full-scale nuclear power applications or the design of new benchmark experiments. Dedicated model benchmark studies investigate a broad range of nuclear reactions and quantities. Examples of these include isotope formation or secondary particle fluxes that result from the interactions of GeV-range hadrons with monoisotopic targets, which can be used to assess the respective systematic uncertainty of models. Such benchmark studies, as well as many nuclear application experiments and simulations carried out by various groups over the last few decades, enable us to draw methodological lessons. In this work, model uncertainty determined based on available experimental data allow us to identify the effects of practitioner expertise as well as the design of codes (user access to micro-scale parameters) on the range of uncertainties. We found that in cases when simulations are performed by code developers or users that are very experienced in performing simulations, the model to experiment quantity ratios generally agree with the limits determined by dedicated benchmark studies. In other cases, the ratios generally tend to be either smaller (underestimation of model error) or larger (overestimation of model error). A plausible explanation of the aforementioned effects is suggested.