General-purpose event generators for LHC physics
read the original abstract
We review the physics basis, main features and use of general-purpose Monte Carlo event generators for the simulation of proton-proton collisions at the Large Hadron Collider. Topics included are: the generation of hard-scattering matrix elements for processes of interest, at both leading and next-to-leading QCD perturbative order; their matching to approximate treatments of higher orders based on the showering approximation; the parton and dipole shower formulations; parton distribution functions for event generators; non-perturbative aspects such as soft QCD collisions, the underlying event and diffractive processes; the string and cluster models for hadron formation; the treatment of hadron and tau decays; the inclusion of QED radiation and beyond-Standard-Model processes. We describe the principal features of the ARIADNE, Herwig++, PYTHIA 8 and SHERPA generators, together with the Rivet and Professor validation and tuning tools, and discuss the physics philosophy behind the proper use of these generators and tools. This review is aimed at phenomenologists wishing to understand better how parton-level predictions are translated into hadron-level events as well as experimentalists wanting a deeper insight into the tools available for signal and background simulation at the LHC.
This paper has not been read by Pith yet.
Forward citations
Cited by 8 Pith papers
-
Nested-GPT for variable-multiplicity parton showers: A case study in the resummation of non-global logarithms
Nested-GPT is an autoregressive Transformer that dynamically generates variable-multiplicity parton showers matching Monte Carlo references for non-global logarithm resummation in the large-Nc limit.
-
Nested-GPT for variable-multiplicity parton showers: A case study in the resummation of non-global logarithms
Nested-GPT is an autoregressive Transformer surrogate that generates variable-multiplicity parton showers while enforcing ordered Markovian branching and matches reference Monte Carlo results for leading-log non-globa...
-
Monte Carlo Event Generation with Continuous Normalizing Flows
Continuous normalizing flows improve unweighting efficiency in Monte Carlo event generation for high-jet-multiplicity collider processes by factors up to 184, with wall-time gains of about ten when combined with coupl...
-
On the reconstruction of kinematic distributions computed with Monte Carlo methods using orthogonal basis functions
A method to approximate kinematic distributions from Monte Carlo events using coefficients of orthogonal basis functions produces smooth curves and removes bin-to-bin fluctuations in subtracted perturbative calculations.
-
An NLO-Matched Initial and Final State Parton Shower on a GPU
GAPS v2 is a GPU-accelerated parton shower for initial and final state emissions with NLO matching that achieves speed and energy performance on par with a 96-core CPU cluster for NLO Z production at the LHC.
-
Studying the Infrared Behaviour of Improved Logarithmic Accuracy Parton Showers with Herwig
Implementing improved logarithmic accuracy parton showers in Herwig reveals that differences in infrared cutoffs have important effects on hadron-level predictions and tunability.
-
An NLO-Matched Initial and Final State Parton Shower on a GPU
GPU port of an NLO-matched parton shower achieves performance parity with a 96-core CPU cluster for LHC Z production simulations.
-
An Introduction to PYTHIA 8.2
PYTHIA 8.2 is a mature C++ event generator that combines hard processes, parton showers, multiparton interactions, and string fragmentation into a complete simulation framework for high-energy collisions.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.