Event Generation with Normalizing Flows

Abstract

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 next-to-leading order QCD.

9 pages, 2 figures, 2 tables; v2: matches Journal version

References in corpus (32)

• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• Event generation with SHERPA 1.1
• A General Algorithm for Calculating Jet Cross Sections in NLO QCD
• Parton distributions from high-precision collider data
• Comix, a new matrix element generator
• Event Generation with Sherpa 2.2
• Cuba - a library for multidimensional numerical integration
• General-purpose event generators for LHC physics
• SHERPA 1.alpha, a proof-of-concept version
• AMEGIC++ 1.0, A Matrix Element Generator In C++
• MINLO: Multi-scale improved NLO
• Systematic event generator tuning for the LHC
• Precise Predictions for W + 4 Jet Production at the Large Hadron Collider
• Weight optimization in multichannel Monte Carlo
• HELAC: a package to compute electroweak helicity amplitudes
• An Algorithm to Compute Born Scattering Amplitudes without Feynman Graphs
• How to GAN LHC Events
• Vegas Revisited: Adaptive Monte Carlo Integration Beyond Factorization
• Precision phenomenology with MCFM
• Color-dressed recursive relations for multi-parton amplitudes
• DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC
• A-NICE-MC: Adversarial Training for MCMC
• Simulation of vector boson plus many jet final states at the high luminosity LHC
• NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport
• Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks
• Lund jet images from generative and cycle-consistent adversarial networks
• Foam: Multi-Dimensional General Purpose Monte Carlo Generator With Self-Adapting Simplical Grid
• Jet cross sections at the LHC and the quest for higher precision
• (MC)**3 -- a Multi-Channel Markov Chain Monte Carlo algorithm for phase-space sampling
• Fast computation of MadGraph amplitudes on graphics processing unit (GPU)
• Thread-Scalable Evaluation of Multi-Jet Observables
• Weak Vector Boson Production with Many Jets at the LHC $\sqrt{s}=13$ TeV

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• Targeting Multi-Loop Integrals with Neural Networks
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• Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge
• PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics
• MadNIS -- Neural Multi-Channel Importance Sampling
• Improved Neural Network Monte Carlo Simulation
• How to Understand Limitations of Generative Networks
• Two Invertible Networks for the Matrix Element Method
• Local Unitarity: a representation of differential cross-sections that is locally free of infrared singularities at any order
• Measuring QCD Splittings with Invertible Networks
• VegasFlow: accelerating Monte Carlo simulation across multiple hardware platforms
• Vector Boson Scattering Processes: Status and Prospects
• Towards a Deep Learning Model for Hadronization
• The MadNIS Reloaded
• PC-Droid: Faster diffusion and improved quality for particle cloud generation
• Efficient phase-space generation for hadron collider event simulation
• Unweighting multijet event generation using factorisation-aware neural networks
• One-loop matrix element emulation with factorisation awareness
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• CaloFlow for CaloChallenge Dataset 1
• CaloDREAM -- Detector Response Emulation via Attentive flow Matching
• Ephemeral Learning -- Augmenting Triggers with Online-Trained Normalizing Flows
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• Precision-Machine Learning for the Matrix Element Method
• A survey of machine learning-based physics event generation
• Multi-variable Integration with a Neural Network
• Unbiased Elimination of Negative Weights in Monte Carlo Samples
• Normalizing Flows for High-Dimensional Detector Simulations
• Calorimeter shower superresolution
• A novel event generator for the automated simulation of neutrino scattering
• A Portable Parton-Level Event Generator for the High-Luminosity LHC
• Differentiable MadNIS-Lite
• Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows
• Exploring phase space with Nested Sampling
• Comparison of Affine and Rational Quadratic Spline Coupling and Autoregressive Flows through Robust Statistical Tests
• Uncertainties associated with GAN-generated datasets in high energy physics
• Accurate Surrogate Amplitudes with Calibrated Uncertainties
• Anomaly detection with flow-based fast calorimeter simulators
• Sampling NNLO QCD phase space with normalizing flows
• OASIS: Optimal Analysis-Specific Importance Sampling for event generation
• Efficient negative-weight elimination in large high-multiplicity Monte Carlo event samples
• Machine Learning Post-Minkowskian Integrals
• TopicFlow: Disentangling quark and gluon jets with normalizing flows
• Generative Invertible Quantum Neural Networks
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• NuHepMC: A standardized event record format for neutrino event generators
• BitHEP -- The Limits of Low-Precision ML in HEP
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• Variance Reduction via Simultaneous Importance Sampling and Control Variates Techniques Using Vegas
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• Towards a Fully Automated Differential ${\text{NNLO}}_{\text{EW}}$ Generator for Lepton Colliders
• The use of Generative Adversarial Networks to characterise new physics in multi-lepton final states at the LHC
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• Event Generation and Density Estimation with Surjective Normalizing Flows
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• Implicit Quantile Networks For Emulation in Jet Physics
• On the simulated kinematic distributions of heavy meson decays
• Data-Driven Predictions for Dark Photon and Millicharged Particle Production