In this paper the current release of the Monte Carlo event generator Sherpa, version 1.1, is presented. Sherpa is a general-purpose tool for the simulation of particle collisions at high-energy colliders. It contains a very flexible tree-level matrix-element generator for the calculation of hard scattering processes within the Standard Model and various new physics models. The emission of additional QCD partons off the initial and final states is described through a parton-shower model. To consistently combine multi-parton matrix elements with the QCD parton cascades the approach of Catani, Krauss, Kuhn and Webber is employed. A simple model of multiple interactions is used to account for underlying events in hadron--hadron collisions. The fragmentation of partons into primary hadrons is described using a phenomenological cluster-hadronisation model. A comprehensive library for simulating tau-lepton and hadron decays is provided. Where available form-factor models and matrix elements are used, allowing for the inclusion of spin correlations; effects of virtual and real QED corrections are included using the approach of Yennie, Frautschi and Suura.
Sherpa is a general-purpose Monte Carlo event generator for the simulation of particle collisions in high-energy collider experiments. We summarize essential features and improvements of the Sherpa 2.2 release series, which is heavily used for event generation in the analysis and interpretation of LHC Run 1 and Run 2 data. We highlight a decade of developments towards ever higher precision in the simulation of particle-collision events.
An increase in theoretical precision of Monte Carlo event generators is typically accompanied by an increased need for computational resources. One major obstacle are negative weighted events, which appear in Monte Carlo simulations with higher perturbative accuracy. While they can be handled somewhat easily in fixed-order calculations, they are a major concern for particle level event simulations. In this article, the origin of negative weights in the S-MC@NLO method is reviewed and mechanisms to reduce the negative weight fraction in simulations with the Sherpa event generator are presented,…
Precise theoretical predictions are vital for the interpretation of Standard Model measurements and facilitate conclusive searches for New Physics phenomena at the LHC. In this contribution I highlight some of the ongoing efforts in the framework of the Sherpa event generator to provide accurate and realistic simulations of Standard Model production processes. This includes the automated evaluation of NLO QCD and NLO EW corrections and their consistent consideration in the parton-shower evolution of scattering events. Particular emphasis is given to examples and applications involving the prod…
Accelerating multijet-merged event generation with neural network matrix element surrogates
Institut für Kern und Teilchenphysik, TU Dresden（德累斯顿工业大学核与粒子物理研究所）；Institut für Theoretische Physik, Georg-August-Universität Göttingen（哥廷根大学理论物理研究所）；Campus-Institut Data Science, Georg-August-Universität Göttingen（哥廷根大学校园数据科学研究所）
We use the SHERPA Monte Carlo generator to simulate the process $e^+e^-\rightarrow\mbox{hadrons}$ using matrix elements with up to six partons in the final state. Two samples of SHERPA events are generated. In the "LO" sample, all final states are generated with leading order matrix elements; in the "NLO" sample, matrix elements for final states with up to four partons are generated at next-to-leading order, while matrix elements for final states with five or six partons are generated at leading order. The resulting samples are then passed through the ALEPH detector simulation. We compare the …
Poor computing efficiency of precision event generators for LHC physics has become a bottleneck for Monte-Carlo event simulation campaigns. We provide solutions to this problem by focusing on two major components of general-purpose event generators: The PDF evaluator and the matrix-element generator. For a typical production setup in the ATLAS experiment, we show that the two can consume about 80% of the total runtime. Using NLO simulations of $pp\to\ell^+\ell^-+\text{jets}$ and $pp\to t\bar{t}+\text{jets}$ as an example, we demonstrate that the computing footprint of LHAPDF and Sherpa can be …
We perform two tunes of the SHERPA Monte Carlo generator for the generation of $e^+e^-\rightarrow\mbox{hadrons}$ using the publicly-available LEP analyses in Rivet. In each of these tunes, we generate events at $\sqrt{s}=91.25\mbox{ GeV}$ using matrix elements for final states containing up to six partons. In the first, "LO" tune, matrix elements for all final states are generated at leading order; in the second, "NLO" tune, matrix elements for final states with up to four partons are generated at next-to-leading order using BlackHat, while those for states with five and six partons are genera…
Photoproduction in general-purpose event generators
University of Jyvaskyla（于韦斯屈莱大学）；Institut für Physik, NAWI, Universität Graz（格拉茨大学NAWI物理研究所）；Teilchenphysik, Fakultät für Physik, Universität Wien（维也纳大学物理学院粒子物理系）；Institute for Particle Physics Phenomenology, Durham University（杜伦大学粒子物理现象学研究所）
Monte Carlo event generators (MCEGs) are the indispensable workhorses of particle physics, bridging the gap between theoretical ideas and first-principles calculations on the one hand, and the complex detector signatures and data of the experimental community on the other hand. All collider physics experiments are dependent on simulated events by MCEG codes such as Herwig, Pythia, Sherpa, POWHEG, and MG5_aMC@NLO to design and tune their detectors and analysis strategies. The development of MCEGs is overwhelmingly driven by a vibrant community of academics at European Universities, who also tra…
Scattering Amplitudes as Programs: Self-Evolving Search for Theory and Event Generation
Department of Applied Mathematics and Theoretical Physics, University of Cambridge（剑桥大学应用数学与理论物理系）；Cavendish Laboratory, University of Cambridge（剑桥大学卡文迪许实验室）
Phenomenology of heavy-flavour jet angularities at hadron colliders
Technical University of Munich（慕尼黑工业大学）；Università di Genova（热那亚大学）；INFN, Sezione di Genova（意大利国家核物理研究所热那亚分部）；CERN（欧洲核子研究中心）；Georg-August-Universität Göttingen（哥廷根大学）
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 often time-consuming, iterative process to meet different experimental needs. In this work, we introduce several optimization formulations and algorithms with new decision criteria for streamlining and automating this process. These algorithms are designed for two formulations: bilevel optimization and robust optimization. Both formulations are applied to the d…
Durham University（杜伦大学）；Institute for Particle Physics Phenomenology（粒子物理现象学研究所）；Institute for Data Science（数据科学研究所）；Università di Torino（都灵大学）；INFN（意大利国家核物理研究院）
Machine learning technology has the potential to dramatically optimise event generation and simulations. We continue to investigate the use of neural networks to approximate matrix elements for high-multiplicity scattering processes. We focus on the case of loop-induced diphoton production through gluon fusion and develop a realistic simulation method that can be applied to hadron collider observables. Neural networks are trained using the one-loop amplitudes implemented in the NJet C++ library and interfaced to the Sherpa Monte Carlo event generator where we perform a detailed study for $2\to…
Normalizing flows are a class of generative models that enable exact likelihood evaluation. While these models have already found various applications in particle physics, normalizing flows are not flexible enough to model many of the peripheral features of collision events. Using the framework of Nielsen et al. (2020), we introduce several surjective and stochastic transform layers to a baseline normalizing flow to improve modelling of permutation symmetry, varying dimensionality and discrete features, which are all commonly encountered in particle physics events. We assess their efficacy in …
KaTie is a parton-level event generator for hadron scattering processes that can deal with partonic initial-state momenta with an explicit transverse momentum dependence causing them to be space-like. Provided with the necessary transverse momentum dependent parton density functions, it calculates the off-shell matrix elements and performs the phase space importance sampling to produce weighted events, for example in the Les Houches Event File format. It can deal with arbitrary processes within the Standard Model, for up to four final-state particles and beyond. Furthermore, it can produce eve…
