Graph Generative Models for Fast Detector Simulations in High Energy Physics

2021/04/05 by Ali Hariri, A. Hariri, Darya Dyachkova +4 · 11 citations
Computer Science · Decision Sciences · Physics and Astronomy · #Artificial intelligence #Big Data Technologies and Applications #Computer science #Detector #Distributed and Parallel Computing Systems #Energy (signal processing) #FOS: Computer and information sciences #FOS: Physical sciences #Generative grammar #Generative model #Graph #High Energy Physics - Experiment (hep-ex) #Machine Learning (cs.LG) #Optics #Particle physics #Particle physics theoretical and experimental studies #Physics #Quantum mechanics #Statistical physics #Theoretical computer science #cs.LG #hep-ex

paper · pdf · doi:10.48550/arxiv.2104.01725

published in arXiv (Cornell University) (Cornell University) · Edited references and corrected typos

openalex publication_date 2021/04/05 · openalex created_date 2021/04/13 · arxiv created 2021/08/24 · arxiv updated 2021/08/26 · openalex updated_date 2026/07/28

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.

Citations

• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
• Semi-Supervised Classification with Graph Convolutional Networks
• Variational Graph Auto-Encoders
• Inductive Representation Learning on Large Graphs
• Junction Tree Variational Autoencoder for Molecular Graph Generation
• Graph Neural Networks: A Review of Methods and Applications
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors
• A Comprehensive Survey on Graph Neural Networks

Related

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• Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning
• Lamarr: LHCb ultra-fast simulation based on machine learning models deployed within Gauss
• Real-Time Analysis of Unstructured Data with Machine Learning on Heterogeneous Architectures
• Machine Learning for the LHCb Simulation