We show how many contemporary issues in event generation can be recast in terms of partonic calculations with a matching scale. This framework is called GenEvA, and a key ingredient is a new notion of phase space which avoids the problem of phase space double-counting by construction and includes a built-in definition of a matching scale. This matching scale can be used to smoothly merge any partonic calculation with a parton shower. The best partonic calculation for a given region of phase space can be determined through physics considerations alone, independent of the algorithmic details of the merging. As an explicit example, we construct a positive-weight partonic calculation for e+e- -> n jets at next-to-leading order (NLO) with leading-logarithmic (LL) resummation. We improve on the NLO/LL result by adding additional higher-multiplicity tree-level (LO) calculations to obtain a merged NLO/LO/LL result. These results are implemented using a new phase space generator introduced in a companion paper [arXiv:0801.4028].
We introduce a new efficient algorithm for phase space generation. A parton shower is used to distribute events across all of multiplicity, flavor, and phase space, and these events can then be reweighted to any desired analytic distribution. To verify this method, we reproduce the e+e- -> n jets tree-level result of traditional matrix element tools. We also show how to improve tree-level matrix elements automatically with leading-logarithmic resummation. This algorithm is particularly useful in the context of a new framework for event generation called GenEvA. In a companion paper [arXiv:0801…
We present an extension of the GENEVA Monte Carlo framework to include multiple parton interactions (MPI) provided by PYTHIA8. This allows us to obtain predictions for underlying-event sensitive measurements in Drell-Yan production, in conjunction with GENEVA's fully-differential NNLO calculation, NNLL' resummation for the 0-jet resolution variable (beam thrust), and NLL resummation for the 1-jet resolution variable. We describe the interface with the parton shower algorithm and MPI model of PYTHIA8, which preserves both the precision of partonic N-jet cross sections in GENEVA as well as the s…
We present an event generator for the process $pp\to \ellν_\ellγ$ at next-to-next-to-leading order (NNLO) in QCD and matched to the PYTHIA8 parton shower. The calculation makes use of the GENEVA framework, which combines a resummed calculation obtained via Soft-Collinear Effective Theory (SCET) with the fixed-order result. We validate the NNLO accuracy of our results by comparing predictions for inclusive quantities with an independent fixed-order calculation, and then present the resummed 0-jettiness spectrum at next-to-next-to-leading logarithmic (NNLL$'$) accuracy. Finally, we compare our p…
We present results for the Higgsstrahlung process within the GENEVA Monte Carlo framework. We combine the fully differential NNLO calculation with the higher-order resummation in the 0-jettiness resolution variable (beam-thrust). The resulting parton-level events are further showered and hadronised by Pythia8. The beam-thrust resummation is carried out to NNLL$'$ accuracy, which consistently incorporates all singular virtual and real NNLO corrections. It thus provides a natural perturbative connection between the NNLO calculation and the parton shower regime, including a systematic assessment …
We present a novel next-to-next-to-leading order (NNLO) QCD calculation matched to parton shower for the production of a pair of $Z$ bosons decaying to four massless leptons, $p p \to \ell^+ \ell^- \ell'^+ \ell'^- + X$, at the LHC. Spin correlations, interferences and off-shell effects are included throughout. Our result is based on the resummed beam-thrust spectrum, which we evaluate at next-to-next-to-leading-logarithmic (NNLL$'_{\mathcal{T}_0}$) accuracy for the first time for this process, and makes use of the GENEVA Monte Carlo framework for the matching to PYTHIA8 shower and hadronisatio…
We report next-to-leading order perturbative QCD predictions of 4 jet event shape variables for the process e+e- \to 4 jets obtained using the general purpose Monte Carlo EERAD2. This program is based on the known `squared' one loop matrix elements for the virtual γ^* \to 4 parton contribution and squared matrix elements for 5 parton production. To combine the two distinct final states numerically we present a hybrid of the commonly used subtraction and slicing schemes based on the colour antenna structure of the final state which can be readily applied to other processes. We have checked that…
LABSMC Monte Carlo event generator is used to simulate Bhabha scattering at high energies. Different sources of radiative corrections are considered. The resulting precision is discussed.
We present an algorithm for unweighted event generation in the partonic process pp -> WZ (j) with leptonic decays at next-to-leading order in alpha_S. Monte Carlo programs for processes such as this frequently generate events with negative weights in certain regions of phase space. For simulations of experimental data one would like to have unweighted events only. We demonstrate how the phase space from the matrix elements can be combined to achieve unweighted event generation using a second stage Monte Carlo integration over a volume of real emissions (jets). Observable quantities are kept fi…
CalcHEP is a package for computation of Feynman diagrams and integration over multi-particle phase space. The main idea prescribed into CalcHEP is to make available passing on from Lagrangians to the final distributions effectively with a high level of automation. This article presents new options of CalcHEP available in version 2.3. They are a) MSSM model with different SUGRA scenarios, b) interface with PDFLIB and implementation of new MRST/CTEQ structure functions, c) realization of approach to structure functions for models with diagonal CKM, d) generation of events and interface with PYTH…
We present the first fully automated implementation of cross-section computation and event generation for loop-induced processes. This work is integrated in the MadGraph5_aMC@NLO framework. We describe the optimisations implemented at the level of the matrix element evaluation, phase space integration and event generation allowing for the simulation of large multiplicity loop-induced processes. Along with some selected differential observables, we illustrate our results with a table showing inclusive cross-sections for all loop-induced hadronic scattering processes with up to three final state…
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…
We have developed a Monte Carlo event generator for non-resonant diphoton ($γγ$) production at hadron collisions in the framework of GR@PPA, which consistently includes processes having additional one jet radiation. The possible double count problem in the generation of radiative processes is avoided by using the LLL subtraction method that we have applied to the weak-boson production processes. The subtraction method has been extended to the final-state QED divergence that appears in the $qg \rightarrow γγ+ q$ process. Because a parton shower (PS) which regularizes the subtracted QED divergen…
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.
Results of searches for SUSY production at CMS in events with third-generation signatures are presented. Along with missing energy, the final states may include hadronic jets with or without b-quark tag, light leptons, and tau leptons. These features serve both to distinguish standard-model components, and sensitivity to those SUSY models that lead to final states rich of heavy-flavored particles.
The generation of unit-weight events for complex scattering processes presents a severe challenge to modern Monte Carlo event generators. Even when using sophisticated phase-space sampling techniques adapted to the underlying transition matrix elements, the efficiency for generating unit-weight events from weighted samples can become a limiting factor in practical applications. Here we present a novel two-staged unweighting procedure that makes use of a neural-network surrogate for the full event weight. The algorithm can significantly accelerate the unweighting process, while it still guarant…
Universität Heidelberg（海德堡大学）；Heidelberg Collaboratory for Image Processing（海德堡图像处理合作实验室）
Following the growing success of generative neural networks in LHC simulations, the crucial question is how to control the networks and assign uncertainties to their event output. We show how Bayesian normalizing flow or invertible networks capture uncertainties from the training and turn them into an uncertainty on the event weight. Fundamentally, the interplay between density and uncertainty estimates indicates that these networks learn functions in analogy to parameter fits rather than binned event counts.
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,…
The cross sections of the Higgs production and the corresponding backgrounds of $e^+e^-$ annihilations at the CEPC (Circular Electron and Positron Collider) are calculated by a Monte-Carlo method, and the beamstruhlung effect at the CEPC is carefully investigated. The potential for new physics beyond the Standard Model at the CEPC is discussed.
I describe the optimizing matrix element generator O'Mega and Wolfgang Kilian's event generator generator WHIZARD. These tools cooperate in the automated production of efficient unweighted event generators for linear collider physics.
In a noncentral heavy-ion collision, the two colliding nuclei have finite angular momentum in the direction perpendicular to the reaction plane. After the collision, a fraction of the total angular momentum is retained in the produced hot quark-gluon matter and is manifested in the form of fluid shear. Such fluid shear creates finite flow vorticity. We study some features of such generated vorticity, including its strength, beam energy dependence, centrality dependence, and spatial distribution.
We propose a technique called Optimal Analysis-Specific Importance Sampling (OASIS) to reduce the number of simulated events required for a high-energy experimental analysis to reach a target sensitivity. We provide recipes to obtain the optimal sampling distributions which preferentially focus the event generation on the regions of phase space with high utility to the experimental analyses. OASIS leads to a conservation of resources at all stages of the Monte Carlo pipeline, including full-detector simulation, and is complementary to approaches which seek to speed-up the simulation pipeline.
We compute the electromagnetic fields generated in heavy-ion collisions by using the HIJING model. Although after averaging over many events only the magnetic field perpendicular to the reaction plane is sizable, we find very strong magnetic and electric fields both parallel and perpendicular to the reaction plane on the event-by-event basis. We study the time evolution and the spatial distribution of these fields. Especially, the electromagnetic response of the quark-gluon plasma can give non-trivial evolution of the electromagnetic fields. The implications of the strong electromagnetic field…
Old Dominion University（欧道明大学）；Jefferson Lab（杰斐逊实验室）；Shandong University（山东大学）
Event generators in high-energy nuclear and particle physics play an important role in facilitating studies of particle reactions. We survey the state-of-the-art of machine learning (ML) efforts at building physics event generators. We review ML generative models used in ML-based event generators and their specific challenges, and discuss various approaches of incorporating physics into the ML model designs to overcome these challenges. Finally, we explore some open questions related to super-resolution, fidelity, and extrapolation for physics event generation based on ML technology.
We present a study for the generation of events from a physical process with deep generative models. The simulation of physical processes requires not only the production of physical events, but also to ensure these events occur with the correct frequencies. We investigate the feasibility of learning the event generation and the frequency of occurrence with Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) to produce events like Monte Carlo generators. We study three processes: a simple two-body decay, the processes $e^+e^-\to Z \to l^+l^-$ and $p p \to t\bar{t} $ incl…
