Bayesian optimization, implemented two ways, finds a six-parameter Pythia8 tune with a lower objective value against ALEPH LEPI data than the Monash default, though the gain is measured on the same data used for fitting.
Bayesian Optimization of Pythia8 Tunes
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abstract
A new tune (set of model parameters) is found for the six most important parameters of the Pythia8 final state parton shower and hadronization model using Bayesian optimization. The tune fits the LEPI data from ALEPH better than the default tune in Pythia8. 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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Bayesian Optimization of Pythia8 Tunes
Bayesian optimization, implemented two ways, finds a six-parameter Pythia8 tune with a lower objective value against ALEPH LEPI data than the Monash default, though the gain is measured on the same data used for fitting.