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Monte Carlo Event Generators are important tools for the understanding of physics at particle colliders like the LHC. In order to best predict a wide variety of observables, the optimization of parameters in the Event Generators based on…

高能物理 - 唯象学 · 物理学 2020-02-19 Johannes Bellm , Leif Gellersen

Monte Carlo event generators contain a large number of parameters that must be determined by comparing the output of the generator with experimental data. Generating enough events with a fixed set of parameter values to enable making such a…

数据分析、统计与概率 · 物理学 2017-04-28 Philip Ilten , Mike Williams , Yunjie Yang

The Monte Carlo event generators (MC) are used for the simulation of different processes in high energy physics. To achieve the best description of the data, the parameters of simulations are adjusted (tuned) with different methods. In this…

高能物理 - 实验 · 物理学 2018-01-23 Fabian Klimpel

Event generators play an important role in all physics programs at the Large Hadron Collider and beyond. Dedicated efforts are required to tune the parameters of event generators to accurately describe data. There are many tuning methods…

高能物理 - 唯象学 · 物理学 2023-10-12 Jaffae Schroff , Xiangyang Ju

Data analyses in hadron collider physics depend on background simulations performed by Monte Carlo (MC) event generators. However, calculational limitations and non-perturbative effects require approximate models with adjustable parameters.…

高能物理 - 唯象学 · 物理学 2011-04-20 Andy Buckley , Hendrik Hoeth , Heiko Lacker , Holger Schulz , Jan Eike von Seggern

The modelling of the formation of colour-singlet hadrons from coloured partons, known as Hadronization, is crucial for generating realistic events in Monte Carlo Event Generators. Due to limited understanding of the non-perturbative regime,…

高能物理 - 唯象学 · 物理学 2025-09-03 Michaela Divisova , Miroslav Myska , Pratixan Sarmah , Andrzej Siódmok

The parameters in Monte Carlo (MC) event generators are tuned on experimental measurements by evaluating the goodness of fit between the data and the MC predictions. The relative importance of each measurement is adjusted manually in an…

The modelling of multiple parton interactions in Monte Carlo event generators is a crucial part not only for the dressing of signal processes but also to describe data with a minimum bias on the event selection. Much work has and will be…

高能物理 - 唯象学 · 物理学 2020-06-24 Johannes Bellm , Stefan Gieseke , Patrick Kirchgaesser

We present the Monte Carlo generator tuning strategy followed, and the tools developed, by the MCnet CEDAR project. We also present new tuning results for the Pythia 6.4 event generator which are based on event shape and hadronisation…

高能物理 - 唯象学 · 物理学 2009-06-02 Andy Buckley , Hendrik Hoeth , Heiko Lacker , Holger Schulz , Eike von Seggern

In this article we describe Professor, a new program for tuning model parameters of Monte Carlo event generators to experimental data by parameterising the per-bin generator response to parameter variations and numerically optimising the…

高能物理 - 唯象学 · 物理学 2010-01-06 Andy Buckley , Hendrik Hoeth , Heiko Lacker , Holger Schulz , Jan Eike von Seggern

New sets of parameters ("tunes") for the underlying-event (UE) modeling of the PYTHIA8, PYTHIA6 and HERWIG++ Monte Carlo event generators are constructed using different parton distribution functions. Combined fits to CMS UE proton-proton…

高能物理 - 实验 · 物理学 2016-03-21 CMS Collaboration

We present a Monte-Carlo implementation of the Statistical Hadronization Model in e+e- collisions. The physical scheme is based on the statistical hadronization of massive clusters produced by the event generator Herwig within the…

高能物理 - 唯象学 · 物理学 2015-06-04 C. Bignamini , F. Becattini , F. Piccinini

This paper presents a new set of parameters ("tunes") for the underlying-event model of the HERWIG 7 event generator. These parameters control the description of multiple-parton interactions (MPI) and colour reconnection in HERWIG 7, and…

高能物理 - 实验 · 物理学 2021-04-15 CMS Collaboration

We apply, for the first time, Bayes Linear Emulation and History Matching to the calibration of non-perturbative models in Monte Carlo event generators. In contrast to the usual approach of "Monte Carlo tuning", History Matching does not…

高能物理 - 唯象学 · 物理学 2026-02-27 Andrew Iskauskas , Max Knobbe , Frank Krauss , Steffen Schumann

The parameters tuning of event generators is a research topic characterized by complex choices: the generator response to parameter variations is difficult to obtain on a theoretical basis, and numerical methods are hardly tractable due to…

计算物理 · 物理学 2021-03-17 Marco Lazzarin , Simone Alioli , Stefano Carrazza

Neutrino oscillation experiments use Monte Carlo event generators to predict neutrino-nucleus interactions. Cross section uncertainties are typically implemented by varying the parameters of the model(s) used in the generator. We study the…

高能物理 - 唯象学 · 物理学 2025-12-03 Jean Wolfs , Chris M. Marshall

We present an investigation of the dependence of searches for boosted Higgs bosons using jet substructure on the perturbative and non-perturbative parameters of the Herwig++ Monte Carlo event generator. Values are presented for a new tune…

高能物理 - 唯象学 · 物理学 2015-06-05 Peter Richardson , David Winn

In this proceedings I discuss the general strategy and impact of tuning Monte-Carlo event generators for physics processes involving top quarks. Special emphasis is put on disinguishing the different usages of event generators in the…

高能物理 - 唯象学 · 物理学 2019-01-16 Marek Schönherr

With the recently increased interest in probabilistic models, the efficiency of an underlying sampler becomes a crucial consideration. Hamiltonian Monte Carlo (HMC) is one popular option for models of this kind. Performance of the method,…

Herwig 7 is a general-purpose Monte Carlo generator of particle collisions comprising both hard perturbative as well as soft phenomenological physics. Herwig is therefore capable to describe the entire final state of hadronized particles in…

高能物理 - 唯象学 · 物理学 2020-12-04 Johannes Bellm , Cody B. Duncan , Stefan Gieseke , Miroslav Myska , Andrzej Siodmok
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