A Bayesian tune of the Herwig Monte Carlo event generator

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dc.contributor.authorLa Cagnina, Salvatore
dc.contributor.authorKröninger, Kevin
dc.contributor.authorKluth, Stefan
dc.contributor.authorVerbytskyi, Andrii
dc.date.accessioned2024-07-30T09:02:59Z
dc.date.available2024-07-30T09:02:59Z
dc.date.issued2023-10-26
dc.description.abstractThe optimisation (tuning) of the free parameters of Monte Carlo event generators by comparing their predictions with data is important since the simulations are used to calculate experimental efficiency and acceptance corrections, or provide predictions for signatures of hypothetical new processes in experiments. We present a tuning procedure that is based on Bayesian reasoning and that allows for a proper statistical interpretation of the results. The parameter space is fully explored using Markov Chain Monte Carlo. We apply the tuning procedure to the Herwig7 event generator with both the cluster and the string hadronization models and a large set of measurements from hadronic Z-boson decays produced at LEP in e+e- collisions. Furthermore, we introduce a coherent propagation of uncertainties from the realm of parameters to the realm of observables and we show the effects of including experimental correlations of the measurements. To allow comparison with the approaches of other groups, we repeat the tuning considering weights for individual measurements.en
dc.identifier.urihttp://hdl.handle.net/2003/42618
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-24454
dc.language.isoende
dc.relation.ispartofseriesJournal of instrumentation;18(10)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/de
dc.subjectAnalysis and statistical methodsen
dc.subjectPattern recognition, cluster finding, calibration and fitting methodsen
dc.subject.ddc530
dc.titleA Bayesian tune of the Herwig Monte Carlo event generatoren
dc.typeTextde
dc.type.publicationtypeArticlede
dcterms.accessRightsopen access
eldorado.secondarypublicationtruede
eldorado.secondarypublication.primarycitationSalvatore La Cagnina et al 2023 JINST 18 P10033de
eldorado.secondarypublication.primaryidentifierhttps://doi.org/10.1088/1748-0221/18/10/P10033de

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