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Optimizing Monte Carlo model parameters with Bayesian statistics

Published on:

4 December 2023

Primary Category:

High Energy Physics - Phenomenology

Paper Authors:

Jason Hirtz,

Jean-Christophe David,

Joseph Cugnon,

Ingo Leya,

José Luís Rodríguez-Sánchez,

Georg Schnabel


Key Details

Method optimizes model parameters to fit data, and quantifies parameter uncertainties

Uses Bayesian statistics - iterative expectation maximization and Gibbs sampling

Applied to nuclear reaction Monte Carlo model INCL/ABLA

Improved model agreement with data by factors of 100 for some reactions

Computationally intensive method requiring careful data selection

AI generated summary

Optimizing Monte Carlo model parameters with Bayesian statistics

This paper presents a method to optimize the free parameters of models to better fit experimental data, and to estimate the uncertainties on those optimized parameters. The method is based on Bayesian statistics, using an iterative algorithm with expectation maximization and Gibbs sampling phases. It alternates between improving the parameter values and estimating the posterior distribution. The authors demonstrate its application to optimize four parameters of the INCL/ABLA Monte Carlo model for simulating nuclear reactions, significantly improving agreement with measured observables. They discuss requirements, optimizations, and limits when applying this general approach.

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