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  • Open Access

Bayesian optimization of pythia8 tunes

Ali Al Kadhim* and Harrison B. Prosper†

Stephen Mrenna‡

  • Computational Science and AI Directorate, Fermilab, Batavia, Illinois, USA

  • *Contact author: aalkadhim@fsu.edu
  • †Contact author: hprosper@fsu.edu
  • ‡Contact author: mrenna@fnal.gov
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Phys. Rev. D 112, 056012 – Published 11 September, 2025

DOI: https://doi.org/10.1103/1hn2-hz4q

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 Large Electron-Positron collider (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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Phys. Rev. D 112, 056012– Published 11 September, 2025
  • Received 21 May 2025
  • Accepted 11 August 2025
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3.

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