Graph Generative Models for Fast Detector Simulations in High Energy Physics

• URL: http://arxiv.org/abs/2104.01725v1
• Date: Mon, 5 Apr 2021 00:27:43 GMT
• Title: Graph Generative Models for Fast Detector Simulations in High Energy Physics
• Authors: Ali Hariri, Darya Dyachkova and Sergei Gleyzer
• Abstract summary: Simulating particle interactions with detectors is time consuming and computationally expensive. HL-LHC upgrade will put a significant strain on the computing infrastructure. We discuss a graph generative model that provides effective reconstruction of LHC events.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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