A Bayesian tune of the Herwig Monte Carlo event generator

The 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 the experiments. We perform the optimisation using a Bayesian approach based on the Markov chain Monte Carlo method which allows to fully explore the posterior distributions of the optimised free parameters. We use the Herwig7 event generator with both the cluster and the string hadronisation models with many different measurements from hadronic Z boson decays produced at LEP in $e^+e^-$ annihilation. We investigate the effects of weighting several measurements, including a model for possible correlations between the measurements, and introduce full error propagation from the optimised parameters to the predictions.