DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC

Abstract

A Generative-Adversarial Network (GAN) based on convolutional neural networks is used to simulate the production of pairs of jets at the LHC. The GAN is trained on events generated using MadGraph5 + Pythia8, and Delphes3 fast detector simulation. We demonstrate that a number of kinematic distributions both at Monte Carlo truth level and after the detector simulation can be reproduced by the generator network with a very good level of agreement. The code can be checked out or forked from the publicly accessible online repository https://gitlab.cern.ch/disipio/DiJetGAN .

arXiv comment17 pages, 8 figures

References in corpus (20)

• The anti-k_t jet clustering algorithm
• A Brief Introduction to PYTHIA 8.1
• DELPHES 3, A modular framework for fast simulation of a generic collider experiment
• A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX
• MadGraph 5 : Going Beyond
• Event generation with SHERPA 1.1
• Matching NLO QCD computations and parton shower simulations
• Herwig 7.0 / Herwig++ 3.0 Release Note
• Merging meets matching in MC@NLO
• Optimal whitening and decorrelation
• Towards Jetography
• CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
• Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
• Jet-Images -- Deep Learning Edition
• Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters
• Merging Multi-leg NLO Matrix Elements with Parton Showers
• Precise simulation of electromagnetic calorimeter showers using a Wasserstein Generative Adversarial Network
• Uncertainties in MEPS@NLO calculations of h+jets
• Searches for Dijet Resonances at Hadron Colliders
• The BSM-AI project: SUSY-AI - Generalizing LHC limits on Supersymmetry with Machine Learning

Cited by in corpus (65)

• Anomaly Detection with Density Estimation
• Event Generation with Normalizing Flows
• How to GAN LHC Events
• Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed
• Machine Learning and LHC Event Generation
• Exploring phase space with Neural Importance Sampling
• Invertible Networks or Partons to Detector and Back Again
• Neural Networks for Full Phase-space Reweighting and Parameter Tuning
• CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows
• How to GAN away Detector Effects
• Understanding Event-Generation Networks via Uncertainties
• Applications and Techniques for Fast Machine Learning in Science
• Event Generators for High-Energy Physics Experiments
• Accelerating Monte Carlo event generation -- rejection sampling using neural network event-weight estimates
• GANplifying Event Samples
• Generative Networks for Precision Enthusiasts
• A Neural Resampler for Monte Carlo Reweighting with Preserved Uncertainties
• A factorisation-aware Matrix element emulator
• Searches for the BSM scenarios at the LHC using decision tree based machine learning algorithms: A comparative study and review of Random Forest, Adaboost, XGboost and LightGBM frameworks
• Optimising simulations for diphoton production at hadron colliders using amplitude neural networks
• Evaluating generative models in high energy physics
• Targeting Multi-Loop Integrals with Neural Networks
• A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty
• Phase Space Sampling and Inference from Weighted Events with Autoregressive Flows
• PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics
• Calomplification -- The Power of Generative Calorimeter Models
• How to Understand Limitations of Generative Networks
• Two Invertible Networks for the Matrix Element Method
• Neural Network-based Top Tagger with Two-Point Energy Correlations and Geometry of Soft Emissions
• The MadNIS Reloaded
• PC-Droid: Faster diffusion and improved quality for particle cloud generation
• Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
• One-loop matrix element emulation with factorisation awareness
• Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoning
• How to GAN Event Subtraction
• CaloDREAM -- Detector Response Emulation via Attentive flow Matching
• Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders
• Precision-Machine Learning for the Matrix Element Method
• Unsupervised Quantum Circuit Learning in High Energy Physics
• Multi-variable Integration with a Neural Network
• Normalizing Flows for High-Dimensional Detector Simulations
• Parameter Estimation using Neural Networks in the Presence of Detector Effects
• Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows
• Non-Parametric Data-Driven Background Modelling using Conditional Probabilities
• Differentiable MadNIS-Lite
• Exploring phase space with Nested Sampling
• Uncertainties associated with GAN-generated datasets in high energy physics
• Accurate Surrogate Amplitudes with Calibrated Uncertainties
• Simulating the Time Projection Chamber responses at the MPD detector using Generative Adversarial Networks
• Physics Validation of Novel Convolutional 2D Architectures for Speeding Up High Energy Physics Simulations
• A Data-driven Event Generator for Hadron Colliders using Wasserstein Generative Adversarial Network
• OASIS: Optimal Analysis-Specific Importance Sampling for event generation
• Classification of Equation of State in Relativistic Heavy-Ion Collisions Using Deep Learning
• Machine Learning Post-Minkowskian Integrals
• How to GAN Event Unweighting
• Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Information
• Top-philic Machine Learning
• LHC Hadronic Jet Generation Using Convolutional Variational Autoencoders with Normalizing Flows
• Learning to discover: expressive Gaussian mixture models for multi-dimensional simulation and parameter inference in the physical sciences
• Variance Reduction via Simultaneous Importance Sampling and Control Variates Techniques Using Vegas
• Extrapolating Jet Radiation with Autoregressive Transformers
• Amplitude Uncertainties Everywhere All at Once
• Event generation and consistency tests with sliced Wasserstein distance in high-energy physics
• Ultra-Fast Generation of Air Shower Images for Imaging Air Cherenkov Telescopes using Generative Adversarial Networks
• Generators and the (Accelerated) Future