Graph Generative Models for Fast Detector Simulations in High Energy Physics
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 26 of its 71 references that are in the map. First posted on arXiv 5 Apr 2021.
Builds on
26 of 71 references.
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14 more
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Citing papers per year: 2022: 1; 2023: 1.
2 papers.