Spacetime colour reconnection in Herwig 7

Bellm, Johannes; Duncan, Cody B.; Gieseke, Stefan; Myška, M.; Siódmok, Andrzej

doi:10.1140/epjc/s10052-019-7533-6

Cited by 29 publications

(27 citation statements)

References 89 publications

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“…Following the good agreement between parton-level and particle-level results established in Ref. [50], and the good agreement between fixed-order and MINLO [51] results established in Ref. [52], the renormalization and factorization scales are set to unweighting efficiency LO QCD NLO QCD (RS) w /wmax n =0 n =1 n =2 n =3 n =4 n =0 n =1 W + + n jets Sherpa 2.8 · 10 −1 3.8 · 10 −2 7.5 · 10 −3 1.5 · 10 −3 8.3 · 10 −4 9.5 · 10 −2 4.5 · 10 −3 NN+NF 6.1 · 10 −1 1.2 · 10 −1 1.0 · 10 −3 1.8 · 10 −3 8.9 · 10 −4 1.6 · 10 −1 4.…”

Section: Comparison To Existing Approachessupporting

confidence: 65%

Event generation with normalizing flows

et al. 2020

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We present a novel integrator based on normalizing flows which can be used to improve the unweighting efficiency of Monte-Carlo event generators for collider physics simulations. In contrast to machine learning approaches based on surrogate models, our method generates the correct result even if the underlying neural networks are not optimally trained. We exemplify the new strategy using the example of Drell-Yan type processes at the LHC, both at leading and partially at nextto-leading order QCD. I. INTRODUCTIONNumerical simulation programs are a cornerstone of collider physics. They are used for the planning of future experiments, analysis of current measurements and, finally, reinterpretation based on an improved theoretical understanding of nature. They employ Monte Carlo methods to link theory and experiment by generating virtual collider events, which can then be analyzed like actual events observed in detectors [1,2].With more and more data available from the Large Hadron Collider (LHC) and the high-luminosity upgrade, the task of simulating collisions at high precision becomes a matter of concern for the high-energy physics community. The projected amount of computational resources falls far short of the needs for precision event generation [3]. Past studies of the scaling behavior of multi-jet simulations have shown that the compute needs are largely determined by the gradually decreasing unweighting efficiency [4,5]. Except for dedicated integrators, which require a detailed understanding of the physics problem at hand, adaptive Monte-Carlo methods seem the only choice to address this problem [6][7][8][9][10][11][12][13].With the rise of machine learning, this topic has seen a resurgence of interest recently. The possibility of using these techniques for integration in high-energy physics was first discussed in Ref. [14]. Boosted Decision Trees and Generative Adversarial Networks (GANs) were investigated as possible general purpose integrators. This new technique improved the integration of non-separable high dimensional functions, for which traditional algorithms failed. The first true physics application was presented in Ref. [15]. The authors used Dense Neural Networks (DNN) in order to perform a variable transformation and demonstrate that they obtain significantly larger efficiencies for three body decay integrals than standard approaches [16]. The major drawback of this method is its computational cost. Since the network acts as a variable transformation, its gradient must be computed for each inference point in order to determine the Jacobian. This becomes computationally heavy for high multiplicity processes.A completely orthogonal approach utilizes machine learning techniques directly for amplitude evaluation [17] or event generation [18][19][20][21][22][23][24]. Training data for these approaches are obtained from traditional event generation techniques, and hence the problem of efficient event generation still remains. In addition, one needs to ensure that the neural networks are trained well ...

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Section: Comparison To Existing Approachessupporting

confidence: 65%

Event generation with normalizing flows

et al. 2020

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“…While height differences are easily modified in the tuning process, shape differences prefer choices with a random colour partner for gluons in the hard process For more details on the implementation and the details of the model, we refer to [44]. Please note that the spacetime picture related model [45] is not included in the current release.…”

Section: Colour Reconnectionmentioning

confidence: 99%

Herwig 7.2 release note

et al. 2020

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A new release of the Monte Carlo event generator Herwig (version 7.2) is now available. This version introduces a number of improvements over the major version 7.0, notably: multi-jet merging with the dipole shower at LO and NLO QCD; spin correlations in both the dipole and angular-ordered parton showers; an improved choice of evolution variable in the angular-ordered parton shower; improvements to mass effects and top decays in the dipole shower, improvements to the simulation of multiple-parton interactions, including diffractive processes; a new model for baryonic colour reconnection; improvements to strangeness production; as well as a new tune of the hadronisation parameters and support for generic Lorentz structures in BSM models. This article illustrates new features of versions 7.1 and 7.2.

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“…Jets are defined using the k T clustering algorithm with R = 0.4, p T,j > 20 GeV and |η j | < 6. Following the good agreement between parton-level and particlelevel results established in [72], and the good agreement between fixed-order and MINLO [73] results established in [74], the renormalization and factorization scales are set toĤ T /2 [18]. Figure 3 shows the scaling behavior of the parton-level calculation during the optimization stage.…”

Section: Numerical Resultsmentioning

confidence: 92%

Simulation of vector boson plus many jet final states at the high luminosity LHC

2019

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We present a novel event generation framework for the efficient simulation of vector boson plus multi-jet backgrounds at the high-luminosity LHC and at possible future hadron colliders. MPI parallelization of parton-level and particle-level event generation and storage of parton-level event information using the HDF5 data format allow us to obtain leading-order merged Monte-Carlo predictions with up to nine jets in the final state. The parton-level event samples generated in this manner correspond to an integrated luminosity of 3ab −1 and are made publicly available for future phenomenological studies.

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Spacetime colour reconnection in Herwig 7

Cited by 29 publications

References 89 publications

Event generation with normalizing flows

Event generation with normalizing flows

Herwig 7.2 release note

Simulation of vector boson plus many jet final states at the high luminosity LHC

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