Graph Generative Models for Fast Detector Simulations in High Energy Physics

2021/04/05 by A. Hariri, Hariri, Ali, Darya Dyachkova +3
Computer Science · Decision Sciences · Physics and Astronomy · #Big Data Technologies and Applications #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #Machine Learning (cs.LG) #Particle physics theoretical and experimental studies

paper · pdf · doi:10.48550/arxiv.2104.01725

openalex publication_date 2021/04/05 · openalex created_date 2021/04/13 · 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
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• Machine Learning for the LHCb Simulation