Machine Learning and LHC Event Generation
High Energy Physics - Phenomenology
2023-04-26 v2 High Energy Physics - Experiment
Abstract
First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance inference as an inverse simulation problem.
Keywords
Cite
@article{arxiv.2203.07460,
title = {Machine Learning and LHC Event Generation},
author = {Anja Butter and Tilman Plehn and Steffen Schumann and Simon Badger and Sascha Caron and Kyle Cranmer and Francesco Armando Di Bello and Etienne Dreyer and Stefano Forte and Sanmay Ganguly and Dorival Gonçalves and Eilam Gross and Theo Heimel and Gudrun Heinrich and Lukas Heinrich and Alexander Held and Stefan Höche and Jessica N. Howard and Philip Ilten and Joshua Isaacson and Timo Janßen and Stephen Jones and Marumi Kado and Michael Kagan and Gregor Kasieczka and Felix Kling and Sabine Kraml and Claudius Krause and Frank Krauss and Kevin Kröninger and Rahool Kumar Barman and Michel Luchmann and Vitaly Magerya and Daniel Maitre and Bogdan Malaescu and Fabio Maltoni and Till Martini and Olivier Mattelaer and Benjamin Nachman and Sebastian Pitz and Juan Rojo and Matthew Schwartz and David Shih and Frank Siegert and Roy Stegeman and Bob Stienen and Jesse Thaler and Rob Verheyen and Daniel Whiteson and Ramon Winterhalder and Jure Zupan},
journal= {arXiv preprint arXiv:2203.07460},
year = {2023}
}
Comments
Review article based on a Snowmass 2021 contribution