Bayesian optimization finds superior tune for parton shower and hadronization model using LEPI data, suggesting enhancements over default parameters.
A new tune (set of model parameters) is found for the six most important parameters of the final state parton shower and hadronization model using Bayesian optimization. The tune fits the Large Electron-Positron collider (LEPI) data from ALEPH better than the default tune in . To the best of our knowledge, we present the most comprehensive application of Bayesian optimization to the tuning of a parton shower and hadronization model using the LEPI data.
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Kadhim et al. (2025) studied this question.
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