https://doi.org/10.1140/epja/s10050-024-01370-y
Regular Article - Theoretical Physics
Parameter optimisation using Bayesian inference for spallation models
1
IRFU, CEA, Université Paris-Saclay, 91191, Gif-sur-Yvette, France
2
Space Research and Planetary Sciences, Physics Institute, University of Bern, Sidlerstrasse 5, 3012, Bern, Switzerland
3
AGO Department, University of Liège, allée du 6 août 19, bâtiment B5, 4000, Liège, Belgium
4
CITENI, Campus Industrial de Ferrol, Universidade da Coruña, 15403, Ferrol, Spain
5
NAPC-Nuclear Data Section, International Atomic Energy Agency, Vienna, Austria
Received:
5
December
2023
Accepted:
27
June
2024
Published online:
19
July
2024
The accuracy and precision of high-energy spallation models are key issues for the design and development of new applications and experiments. We present a method to estimate model parameters and associated uncertainties by leveraging the Bayesian version of the Generalised Least Squares method, which enables us to incorporate prior knowledge on the parameter values. This approach is designed to adjust parameters based on experimental data, accounting for experimental uncertainty information, and providing uncertainties for all adjusted parameters. This approach is designed in order both to improve the accuracy of models through the modification of free parameters of these models, which results in a better reproduction of experimental data, and to estimate the uncertainties of these parameters and, by extension, their impacts on the model output. We aim at demonstrating the Generalised Least Square method can be applied in the case of Monte Carlo models. We present a proof-of-concept for Monte Carlo models in the specific case of nuclear physics with the model combination INCL/ABLA. We discuss the challenges in the application of this method to high-energy spallation models, notably the large runtime and the stochasticity of the models. Our results indicate this framework can also be applied to analogous situations where parameters of a computationally expensive Monte Carlo code should be inferred/improved.
© The Author(s) 2024
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