Graph Generative Models for Fast Detector Simulations in High Energy Physics
This addresses a critical computing bottleneck for the high-energy physics community, enabling faster simulations for the HL-LHC upgrade, though it appears incremental as it builds on existing machine learning approaches.
The paper tackles the problem of slow and computationally expensive particle detector simulations for the High-Luminosity Large Hadron Collider by proposing a graph generative model, which provides effective reconstruction of LHC events to enable faster simulations without sacrificing accuracy.
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.