A Bayesian tune of the Herwig Monte Carlo event generator

Abstract

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 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.

References in corpus (20)

• MadGraph 5 : Going Beyond
• Herwig++ Physics and Manual
• Scattering Amplitudes with Open Loops
• Herwig 7.0 / Herwig++ 3.0 Release Note
• Tuning PYTHIA 8.1: the Monash 2013 Tune
• General-purpose event generators for LHC physics
• Robust Independent Validation of Experiment and Theory: Rivet version 3
• Systematic event generator tuning for the LHC
• Study of the fragmentation of b quarks into B mesons at the Z peak
• Measurement of the top quark mass in the $t\bar{t}\to$ lepton+jets channel from $\sqrt{s}=8$ TeV ATLAS data and combination with previous results
• QCD Analyses and determinations of alpha-s in e+e- Annihilation at energies between 35 and 189 GeV
• Improving NLO-parton shower matched simulations with higher order matrix elements
• Neural Networks for Full Phase-space Reweighting and Parameter Tuning
• The HepMC3 Event Record Library for Monte Carlo Event Generators
• EFTfitter---A tool for interpreting measurements in the context of effective field theories
• Event generator tuning using Bayesian optimization
• Apprentice for Event Generator Tuning
• High dimensional parameter tuning for event generators
• Revisiting Radiation Patterns in $e^{+}{e}^{-}$ Collisions
• MCNNTUNES: tuning Shower Monte Carlo generators with machine learning

Cited by in corpus (3)

• Measurement of groomed event shape observables in deep-inelastic electron-proton scattering at HERA
• Road map for the tuning of hadronic interaction models with accelerator-based and astroparticle data
• Monte Carlo Event Generators for Future Lepton Colliders