How to GAN Event Unweighting

Backes, Mathias; Butter, Anja; Plehn, Tilman; Winterhalder, Ramon

doi:10.21468/SciPostPhys.10.4.089

SciPost Physics

How to GAN Event Unweighting

Mathias Backes, Anja Butter, Tilman Plehn, Ramon Winterhalder

SciPost Phys. 10, 089 (2021) · published 23 April 2021

doi: 10.21468/SciPostPhys.10.4.089
pdf
Submissions/Reports

Abstract

Event generation with neural networks has seen significant progress recently. The big open question is still how such new methods will accelerate LHC simulations to the level required by upcoming LHC runs. We target a known bottleneck of standard simulations and show how their unweighting procedure can be improved by generative networks. This can, potentially, lead to a very significant gain in simulation speed.

TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhys.10.4.089
TI  - How to GAN Event Unweighting
PY  - 2021/04/23
UR  - https://scipost.org/SciPostPhys.10.4.089
JF  - SciPost Physics
JA  - SciPost Phys.
VL  - 10
IS  - 4
SP  - 089
A1  - Backes, Mathias
AU  - Butter, Anja
AU  - Plehn, Tilman
AU  - Winterhalder, Ramon
AB  - Event generation with neural networks has seen significant progress recently. The big open question is still how such new methods will accelerate LHC simulations to the level required by upcoming LHC runs. We target a known bottleneck of standard simulations and show how their unweighting procedure can be improved by generative networks. This can, potentially, lead to a very significant gain in simulation speed.
ER  -

@Article{10.21468/SciPostPhys.10.4.089,
	title={{How to GAN Event Unweighting}},
	author={Mathias Backes and Anja Butter and Tilman Plehn and Ramon Winterhalder},
	journal={SciPost Phys.},
	volume={10},
	pages={089},
	year={2021},
	publisher={SciPost},
	doi={10.21468/SciPostPhys.10.4.089},
	url={https://scipost.org/10.21468/SciPostPhys.10.4.089},
}

Cited by 26

Barman et al., Top-philic machine learning
Eur. Phys. J. Spec. Top. 233, 2497 (2024) [Crossref]
Mattelaer et al., Speeding up MadGraph5_aMC@NLO
Eur. Phys. J. C 81, 435 (2021) [Crossref]
Bieringer et al., Calomplification — the power of generative calorimeter models
J. Inst. 17, P09028 (2022) [Crossref]
Das et al., How to understand limitations of generative networks
SciPost Phys. 16, 031 (2024) [Crossref]
Butter et al., Two invertible networks for the matrix element method
SciPost Phys. 15, 094 (2023) [Crossref]
Nachman et al., Elsa: enhanced latent spaces for improved collider simulations
Eur. Phys. J. C 83, 843 (2023) [Crossref]
Bellagente et al., Understanding Event-Generation Networks via Uncertainties
SciPost Phys. 13, 003 (2022) [Crossref]
Baldi et al., How to GAN Higher Jet Resolution
SciPost Phys. 13, 064 (2022) [Crossref]
Arratia et al., Publishing unbinned differential cross section results
J. Inst. 17, P01024 (2022) [Crossref]
Butter, Normalizing Flows for LHC Theory
J. Phys.: Conf. Ser. 2438, 012004 (2023) [Crossref]
Maître et al., Multi-variable integration with a neural network
J. High Energ. Phys. 2023, 221 (2023) [Crossref]
Butter et al., Generative networks for precision enthusiasts
SciPost Phys. 14, 078 (2023) [Crossref]
Campbell et al., Event generators for high-energy physics experiments
SciPost Phys. 16, 130 (2024) [Crossref]
Ilten et al., Modeling hadronization using machine learning
SciPost Phys. 14, 027 (2023) [Crossref]
Bravo-Prieto et al., Style-based quantum generative adversarial networks for Monte Carlo events
Quantum 6, 777 777 (2022) [Crossref]
Winterhalder et al., Targeting multi-loop integrals with neural networks
SciPost Phys. 12, 129 (2022) [Crossref]
Maître et al., One-loop matrix element emulation with factorisation awareness
J. High Energ. Phys. 2023, 159 (2023) [Crossref]
Maître et al., A factorisation-aware Matrix element emulator
J. High Energ. Phys. 2021, 66 (2021) [Crossref]
Aylett-Bullock et al., Optimising simulations for diphoton production at hadron colliders using amplitude neural networks
J. High Energ. Phys. 2021, 66 (2021) [Crossref]
Yallup et al., Exploring phase space with nested sampling
Eur. Phys. J. C 82, 678 (2022) [Crossref]
Heimel et al., The MadNIS reloaded
SciPost Phys. 17, 023 (2024) [Crossref]
Heimel et al., Precision-machine learning for the matrix element method
SciPost Phys. 17, 129 (2024) [Crossref]
Butter et al., Kicking it off(-shell) with direct diffusion
SciPost Phys. Core 7, 064 (2024) [Crossref]
Danziger et al., Accelerating Monte Carlo event generation -- rejection sampling using neural network event-weight estimates
SciPost Phys. 12, 164 (2022) [Crossref]
Backes et al., An unfolding method based on conditional invertible neural networks (cINN) using iterative training
SciPost Phys. Core 7, 007 (2024) [Crossref]
Butter et al., Machine learning and LHC event generation
SciPost Phys. 14, 079 (2023) [Crossref]

Authors / Affiliation: mappings to Contributors and Organizations

See all Organizations.

¹ Mathias Backes,
¹ Anja Butter,
¹ Tilman Plehn,
¹ Ramon Winterhalder

¹ Ruprecht-Karls-Universität Heidelberg / Heidelberg University

Funders for the research work leading to this publication