Extrapolating Jet Radiation with Autoregressive Transformers

Abstract

Generative networks are an exciting tool for fast LHC event fixed number of particles. Autoregressive transformers allow us to generate events containing variable numbers of particles, very much in line with the physics of QCD jet radiation, and offer the possibility to generalize to higher multiplicities. We show how transformers can learn a factorized likelihood for jet radiation and extrapolate in terms of the number of generated jets. For this extrapolation, bootstrapping training data and training with modifications of the likelihood loss can be used.

v1: 31 pages, 4 tables, 15 figures; v2: 31 pages, 4 tables, 16 figures, accepted for publication at SciPost Physics

References in corpus (92)

• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• The anti-k_t jet clustering algorithm
• An Introduction to PYTHIA 8.2
• FastJet user manual
• What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
• Matching NLO QCD computations with Parton Shower simulations: the POWHEG method
• MadGraph 5 : Going Beyond
• Herwig 7.0 / Herwig++ 3.0 Release Note
• CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
• Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters
• Event Generation with Normalizing Flows
• How to GAN LHC Events
• Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed
• Precise simulation of electromagnetic calorimeter showers using a Wasserstein Generative Adversarial Network
• Machine Learning and LHC Event Generation
• AtlFast3: the next generation of fast simulation in ATLAS
• JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics
• Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics
• i-flow: High-dimensional Integration and Sampling with Normalizing Flows
• Score-based Generative Models for Calorimeter Shower Simulation
• Exploring phase space with Neural Importance Sampling
• Invertible Networks or Partons to Detector and Back Again
• CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows
• DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC
• How to GAN away Detector Effects
• Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)
• Understanding Event-Generation Networks via Uncertainties
• Accelerating Monte Carlo event generation -- rejection sampling using neural network event-weight estimates
• GANplifying Event Samples
• Generative Networks for Precision Enthusiasts
• A factorisation-aware Matrix element emulator
• CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation
• Optimising simulations for diphoton production at hadron colliders using amplitude neural networks
• Understanding Jet Scaling and Jet Vetos in Higgs Searches
• Targeting Multi-Loop Integrals with Neural Networks
• Deep-Learning Jets with Uncertainties and More
• Scaling Patterns for QCD Jets
• Calomplification -- The Power of Generative Calorimeter Models
• MadNIS -- Neural Multi-Channel Importance Sampling
• L2LFlows: Generating High-Fidelity 3D Calorimeter Images
• Using neural networks for efficient evaluation of high multiplicity scattering amplitudes
• Per-Object Systematics using Deep-Learned Calibration
• Stop on Top
• How to Understand Limitations of Generative Networks
• PanScales showers for hadron collisions: all-order validation
• Two Invertible Networks for the Matrix Element Method
• Measuring QCD Splittings with Invertible Networks
• CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds
• Modern Machine Learning for LHC Physicists
• Binary JUNIPR: an interpretable probabilistic model for discrimination
• ν-Flows: Conditional Neutrino Regression
• Inductive Simulation of Calorimeter Showers with Normalizing Flows
• Learning the language of QCD jets with transformers
• The MadNIS Reloaded
• PC-Droid: Faster diffusion and improved quality for particle cloud generation
• OmniJet-$\alpha$: The first cross-task foundation model for particle physics
• Lectures on LHC Physics
• Efficient phase-space generation for hadron collider event simulation
• Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models
• Improving Generative Model-based Unfolding with Schrödinger Bridges
• Returning CP-Observables to The Frames They Belong
• One-loop matrix element emulation with factorisation awareness
• Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoning
• End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics
• Learning Likelihood Ratios with Neural Network Classifiers
• CaloDREAM -- Detector Response Emulation via Attentive flow Matching
• The Landscape of Unfolding with Machine Learning
• ${\nu }^2$-Flows: Fast and improved neutrino reconstruction in multi-neutrino final states with conditional normalizing flows
• Precision-Machine Learning for the Matrix Element Method
• ELSA -- Enhanced latent spaces for improved collider simulations
• A Lorentz-Equivariant Transformer for All of the LHC
• Introduction to the PanScales framework, version 0.1
• Normalizing Flows for High-Dimensional Detector Simulations
• Refining Fast Calorimeter Simulations with a Schrödinger Bridge
• Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows
• Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics
• Differentiable MadNIS-Lite
• Loop Amplitudes from Precision Networks
• Generative Machine Learning for Detector Response Modeling with a Conditional Normalizing Flow
• CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
• Calibrating Bayesian Generative Machine Learning for Bayesiamplification
• An unfolding method based on conditional Invertible Neural Networks (cINN) using iterative training
• Accelerating HEP simulations with Neural Importance Sampling
• PIPPIN: Generating variable length full events from partons
• Event generation with Sherpa 3
• How to GAN Event Unweighting
• Interpolating amplitudes
• Optimal Equivariant Architectures from the Symmetries of Matrix-Element Likelihoods
• Is Tokenization Needed for Masked Particle Modelling?
• Precision calibration of calorimeter signals in the ATLAS experiment using an uncertainty-aware neural network
• Efficient precision simulation of processes with many-jet final states at the LHC
• The Alaric parton shower for hadron colliders

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