How to GAN LHC events

Butter, Anja; Plehn, Tilman; Winterhalder, Ramon

doi:10.21468/scipostphys.7.6.075

Cited by 165 publications

(161 citation statements)

References 31 publications

Supporting

Mentioning

161

Contrasting

Order By: Relevance

“…Here, the authors focus on pp → 2 jet processes, and implement an Artificial Neural Network point selection (NNPS) scheme for selecting training data based on the points the network struggles to learn the most. In addition, there has been much work on the use of Generative Adversarial Networks (GANs) [14], and other generative models, for event generation [15][16][17][18][19][20][21], while there has been little work addressing the issue of explicit matrix element approximation [22].…”

Section: Introductionmentioning

confidence: 99%

Using neural networks for efficient evaluation of high multiplicity scattering amplitudes

Badger

Bullock

2020

J. High Energ. Phys.

View full text Add to dashboard Cite

Precision theoretical predictions for high multiplicity scattering rely on the evaluation of increasingly complicated scattering amplitudes which come with an extremely high CPU cost. For state-of-the-art processes this can cause technical bottlenecks in the production of fully differential distributions. In this article we explore the possibility of using neural networks to approximate multi-variable scattering amplitudes and provide efficient inputs for Monte Carlo integration. We focus on QCD corrections to e + e − → jets up to one-loop and up to five jets. We demonstrate reliable interpolation when a series of networks are trained to amplitudes that have been divided into sectors defined by their infrared singularity structure. Complete simulations for one-loop distributions show speed improvements of at least an order of magnitude over a standard approach.

show abstract

Section: Introductionmentioning

confidence: 99%

Using neural networks for efficient evaluation of high multiplicity scattering amplitudes

Badger

Bullock

2020

J. High Energ. Phys.

View full text Add to dashboard Cite

show abstract

“…While the benchmark points representing a cluster are isolated points in the parameter space, the procedure we propose here allows us to associate certain shapes more straightforwardly with distinct regions in the parameter space. The application of machine learning techniques in high energy physics, in particular to constrain the EFT/new physics parameter space, has been brought forward already some time ago [68][69][70][71], with successful applications in jet and top quark identification [72][73][74][75][76][77][78][79][80][81], new physics searches [70,71,[82][83][84][85][86][87][88][89][90] and PDFs [91]. Shape analysis with machine learning has been applied already to constrain anomalous Higgs-vector boson couplings in HZ production [92].…”

Section: Introductionmentioning

confidence: 99%

Exploring anomalous couplings in Higgs boson pair production through shape analysis

Capozi

Heinrich

2020

J. High Energ. Phys.

View full text Add to dashboard Cite

We classify shapes of Higgs boson pair invariant mass distributions m hh , calculated at NLO with full top quark mass dependence, and visualise how distinct classes of shapes relate to the underlying coupling parameter space. Our study is based on a five-dimensional parameter space relevant for Higgs boson pair production in a non-linear Effective Field Theory framework. We use two approaches: an analysis based on predefined shape types and a classification into shape clusters based on unsupervised learning. We find that our method based on unsupervised learning is able to capture shape features very well and therefore allows a more detailed study of the impact of anomalous couplings on the m hh shape compared to more conventional approaches to a shape analysis.

show abstract

“…However, very recently there has been significant interest to employ modern machinelearning techniques to the problem of phase space sampling in particle physics, cf. [27,28,29,30,31]. The tremendous advances in the field of machine learning, driven from very different applications such as image generation or light-transport simulation, also fuel the work we present here.…”

Section: Introductionmentioning

confidence: 77%

Exploring phase space with Neural Importance Sampling

et al. 2020

View full text Add to dashboard Cite

We present a novel approach for the integration of scattering cross sections and the generation of partonic event samples in high-energy physics. We propose an importance sampling technique capable of overcoming typical deficiencies of existing approaches by incorporating neural networks. The method guarantees full phase space coverage and the exact reproduction of the desired target distribution, in our case given by the squared transition matrix element. We study the performance of the algorithm for a few representative examples, including top-quark pair production and gluon scattering into three-and four-gluon final states.

show abstract

How to GAN LHC events

Abstract: Event generation for the LHC can be supplemented by generative adversarial networks, which generate physical events and avoid highly inefficient event unweighting. For top pair production we show how such a network describes

Cited by 165 publications

References 31 publications

Using neural networks for efficient evaluation of high multiplicity scattering amplitudes

Using neural networks for efficient evaluation of high multiplicity scattering amplitudes

Exploring anomalous couplings in Higgs boson pair production through shape analysis

Exploring phase space with Neural Importance Sampling

Contact Info

Product

Resources

About