Machine learning and LHC event generation

Butter, Anja; Plehn, Tilman; Schumann, Steffen; Badger, Simon; Caron, Sascha; Cranmer, Kyle; Di Bello, Francesco Armando; Dreyer, Etienne; Forte, Stefano; Ganguly, Sanmay; Gonçalves, Dorival; Gross, Eilam; Heimel, Theo; Heinrich, Gudrun; Heinrich, Lukas; Held, Alexander; Höche, Stefan; Howard, Jessica N.; Ilten, Philip; Isaacson, Joshua; Janßen, Timo; Jones, Stephen; Kado, Marumi; Kagan, Michael; Kasieczka, Gregor; Kling, Felix; Kraml, Sabine; Krause, Claudius; Krauss, Frank; Kröninger, Kevin; Barman, Rahool Kumar; Luchmann, Michel; Magerya, Vitaly; Maitre, Daniel; Malaescu, Bogdan; Maltoni, Fabio; Martini, Till; Mattelaer, Olivier; Nachman, Benjamin; Pitz, Sebastian; Rojo, Juan; Schwartz, Matthew; Shih, David; Siegert, Frank; Stegeman, Roy; Stienen, Bob; Thaler, Jesse; Verheyen, Rob; Whiteson, Daniel; Winterhalder, Ramon; Zupan, Jure

doi:10.21468/scipostphys.14.4.079

Machine learning and LHC event generation

Butter, Anja; Plehn, Tilman; Schumann, Steffen; Badger, Simon; Caron, Sascha; Cranmer, Kyle; Di Bello, Francesco Armando; Dreyer, Etienne; Forte, Stefano; Ganguly, Sanmay; Gonçalves, Dorival; Gross, Eilam; Heimel, Theo; Heinrich, Gudrun; Heinrich, Lukas; Held, Alexander; Höche, Stefan; Howard, Jessica N.; Ilten, Philip; Isaacson, Joshua; Janßen, Timo; Jones, Stephen; Kado, Marumi; Kagan, Michael; Kasieczka, Gregor; Kling, Felix; Kraml, Sabine; Krause, Claudius; Krauss, Frank; Kröninger, Kevin; Barman, Rahool Kumar; Luchmann, Michel; Magerya, Vitaly; Maitre, Daniel; Malaescu, Bogdan; Maltoni, Fabio; Martini, Till; Mattelaer, Olivier; Nachman, Benjamin; Pitz, Sebastian; Rojo, Juan; Schwartz, Matthew; Shih, David; Siegert, Frank; Stegeman, Roy; Stienen, Bob; Thaler, Jesse; Verheyen, Rob; Whiteson, Daniel; Winterhalder, Ramon; Zupan, Jure

Authors

Anja Butter

Tilman Plehn

Steffen Schumann

Dr Simon Badger simon.d.badger@durham.ac.uk
Visiting Professor

Sascha Caron

Kyle Cranmer

Francesco Armando Di Bello

Etienne Dreyer

Stefano Forte

Sanmay Ganguly

Dorival Gonçalves

Eilam Gross

Theo Heimel

Gudrun Heinrich

Lukas Heinrich

Alexander Held

Stefan Höche

Jessica N. Howard

Philip Ilten

Joshua Isaacson

Timo Janßen

Dr Stephen Jones stephen.jones@durham.ac.uk
Associate Professor

Marumi Kado

Michael Kagan

Gregor Kasieczka

Felix Kling

Sabine Kraml

Claudius Krause

Professor Frank Krauss frank.krauss@durham.ac.uk
Professor

Kevin Kröninger

Rahool Kumar Barman

Michel Luchmann

Vitaly Magerya

Professor Daniel Maitre daniel.maitre@durham.ac.uk
Professor

Bogdan Malaescu

Fabio Maltoni

Till Martini

Olivier Mattelaer

Benjamin Nachman

Sebastian Pitz

Juan Rojo

Matthew Schwartz

David Shih

Frank Siegert

Roy Stegeman

Bob Stienen

Jesse Thaler

Rob Verheyen

Daniel Whiteson

Ramon Winterhalder

Jure Zupan

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.

Citation

Butter, A., Plehn, T., Schumann, S., Badger, S., Caron, S., Cranmer, K., …Zupan, J. (2023). Machine learning and LHC event generation. SciPost Physics, 14(4), Article 079. https://doi.org/10.21468/scipostphys.14.4.079

Journal Article Type	Article
Acceptance Date	Feb 9, 2023
Online Publication Date	Apr 21, 2023
Publication Date	Apr 21, 2023
Deposit Date	Feb 19, 2024
Publicly Available Date	Feb 19, 2024
Journal	SciPost Physics
Print ISSN	2542-4653
Publisher	SciPost
Peer Reviewed	Peer Reviewed
Volume	14
Issue	4
Article Number	079
DOI	https://doi.org/10.21468/scipostphys.14.4.079
Keywords	General Physics and Astronomy
Public URL	https://durham-repository.worktribe.com/output/2269736