PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics

Abstract

In this paper, we present a new method to efficiently generate jets in High Energy Physics called PC-JeDi. This method utilises score-based diffusion models in conjunction with transformers which are well suited to the task of generating jets as particle clouds due to their permutation equivariance. PC-JeDi achieves competitive performance with current state-of-the-art methods across several metrics that evaluate the quality of the generated jets. Although slower than other models, due to the large number of forward passes required by diffusion models, it is still substantially faster than traditional detailed simulation. Furthermore, PC-JeDi uses conditional generation to produce jets with a desired mass and transverse momentum for two different particles, top quarks and gluons.

arXiv comment30 pages, 25 figures, 5 tables

References in corpus (54)

• The anti-k_t jet clustering algorithm
• Denoising Diffusion Probabilistic Models
• Hierarchical Text-Conditional Image Generation with CLIP Latents
• Diffusion Models Beat GANs on Image Synthesis
• Score-Based Generative Modeling through Stochastic Differential Equations
• Generative Modeling by Estimating Gradients of the Data Distribution
• Particle-flow reconstruction and global event description with the CMS detector
• Identifying Boosted Objects with N-subjettiness
• Classifier-Free Diffusion Guidance
• ParticleNet: Jet Tagging via Particle Clouds
• Energy Correlation Functions for Jet Substructure
• CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
• Elucidating the Design Space of Diffusion-Based Generative Models
• Jet Substructure at the Large Hadron Collider: Experimental Review
• DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps
• Jet-Images -- Deep Learning Edition
• Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters
• Deep-learning Top Taggers or The End of QCD?
• Energy Flow Networks: Deep Sets for Particle Jets
• The Machine Learning Landscape of Top Taggers
• Power Counting to Better Jet Observables
• Event Generation with Normalizing Flows
• Deep-learned Top Tagging with a Lorentz Layer
• How to GAN LHC Events
• Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
• Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed
• Energy flow polynomials: A complete linear basis for jet substructure
• Pulling Out All the Tops with Computer Vision and Deep Learning
• JEDI-net: a jet identification algorithm based on interaction networks
• The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider
• An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging
• Precise simulation of electromagnetic calorimeter showers using a Wasserstein Generative Adversarial Network
• AtlFast3: the next generation of fast simulation in ATLAS
• Performance of top-quark and $W$-boson tagging with ATLAS in Run 2 of the LHC
• Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics
• Score-based Generative Models for Calorimeter Shower Simulation
• Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem
• 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
• Deep Sets
• ReDecay: A novel approach to speed up the simulation at LHCb
• Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters
• Evaluating generative models in high energy physics
• Point Cloud Transformers applied to Collider Physics
• EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets
• Equivariant Energy Flow Networks for Jet Tagging
• Particle Transformer for Jet Tagging
• Learning to Simulate High Energy Particle Collisions from Unlabeled Data
• NormFormer: Improved Transformer Pretraining with Extra Normalization
• Particle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup description
• New directions for surrogate models and differentiable programming for High Energy Physics detector simulation
• Particle Convolution for High Energy Physics
• Turbo-Sim: a generalised generative model with a physical latent space

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• The MadNIS Reloaded
• Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
• High-dimensional and Permutation Invariant Anomaly Detection
• CaloDREAM -- Detector Response Emulation via Attentive flow Matching
• Precision-Machine Learning for the Matrix Element Method
• A Method to Simultaneously Facilitate All Jet Physics Tasks
• Normalizing Flows for High-Dimensional Detector Simulations
• Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows
• Differentiable MadNIS-Lite
• Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion
• Generalizing to new geometries with Geometry-Aware Autoregressive Models (GAAMs) for fast calorimeter simulation
• PIPPIN: Generating variable length full events from partons
• Effectiveness of denoising diffusion probabilistic models for fast and high-fidelity whole-event simulation in high-energy heavy-ion experiments
• Describing Hadronization via Histories and Observables for Monte-Carlo Event Reweighting
• Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Information
• Parnassus: An Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction
• Ultra fast, event-by-event heavy-ion simulations for next generation experiments
• Progress in End-to-End Optimization of Detectors for Fundamental Physics with Differentiable Programming
• LHC Hadronic Jet Generation Using Convolutional Variational Autoencoders with Normalizing Flows
• Toward a foundation model for heavy-ion collision experiments based on point-cloud diffusion
• BitHEP -- The Limits of Low-Precision ML in HEP
• Choose Your Diffusion: Efficient and flexible ways to accelerate the diffusion model in fast high energy physics simulation
• TrackFormers: In Search of Transformer-Based Particle Tracking for the High-Luminosity LHC Era
• CaloHadronic: a diffusion model for the generation of hadronic showers
• Enhancing generalization in high energy physics using white-box adversarial attacks
• Variational inference for pile-up removal at hadron colliders with diffusion models