Graph Generative Models for Fast Detector Simulations in High Energy Physics

Abstract

Accurate and fast simulation of particle physics processes is crucial for the high-energy physics community. Simulating particle interactions with detectors is both time consuming and computationally expensive. With the proton-proton collision energy of 13 TeV, the Large Hadron Collider is uniquely positioned to detect and measure the rare phenomena that can shape our knowledge of new interactions. The High-Luminosity Large Hadron Collider (HL-LHC) upgrade will put a significant strain on the computing infrastructure due to increased event rate and levels of pile-up. Simulation of high-energy physics collisions needs to be significantly faster without sacrificing the physics accuracy. Machine learning approaches can offer faster solutions, while maintaining a high level of fidelity. We discuss a graph generative model that provides effective reconstruction of LHC events, paving the way for full detector level fast simulation for HL-LHC.

arXiv commentEdited references and corrected typos

References in corpus (28)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• PYTHIA 6.4 Physics and Manual
• PYTHIA 6.2 Physics and Manual
• Semi-Supervised Classification with Graph Convolutional Networks
• Inductive Representation Learning on Large Graphs
• Neural Message Passing for Quantum Chemistry
• DELPHES 3, A modular framework for fast simulation of a generic collider experiment
• MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
• Graph Neural Networks: A Review of Methods and Applications
• Variational Graph Auto-Encoders
• Junction Tree Variational Autoencoder for Molecular Graph Generation
• Muon Decay and Physics Beyond the Standard Model
• Discrete Signal Processing on Graphs: Sampling Theory
• Interaction Networks for Learning about Objects, Relations and Physics
• Physics Beyond Colliders at CERN: Beyond the Standard Model Working Group Report
• Horovod: fast and easy distributed deep learning in TensorFlow
• ParticleNet: Jet Tagging via Particle Clouds
• Deep Learning and its Application to LHC Physics
• Pileup Per Particle Identification
• 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
• Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed
• Learning representations of irregular particle-detector geometry with distance-weighted graph networks
• The Metric Space of Collider Events
• Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors
• Deep learning jet modifications in heavy-ion collisions
• End-to-End Physics Event Classification with CMS Open Data: Applying Image-Based Deep Learning to Detector Data for the Direct Classification of Collision Events at the LHC

Cited by in corpus (4)

• Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
• Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoning
• Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders
• Set-Conditional Set Generation for Particle Physics