Loop amplitudes from precision networks

Badger, Simon; Butter, Anja; Luchmann, Michel; Pitz, Sebastian; Plehn, Tilman

doi:10.21468/SciPostPhysCore.6.2.034

SciPost Physics Core

Loop amplitudes from precision networks

Simon Badger, Anja Butter, Michel Luchmann, Sebastian Pitz, Tilman Plehn

SciPost Phys. Core 6, 034 (2023) · published 25 April 2023

doi: 10.21468/SciPostPhysCore.6.2.034
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Abstract

Evaluating loop amplitudes is a time-consuming part of LHC event generation. For di-photon production with jets we show that simple, Bayesian networks can learn such amplitudes and model their uncertainties reliably. A boosted training of the Bayesian network further improves the uncertainty estimate and the network precision in critical phase space regions. In general, boosted network training of Bayesian networks allows us to move between fit-like and interpolation-like regimes of network training.

TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhysCore.6.2.034
TI  - Loop amplitudes from precision networks
PY  - 2023/04/25
UR  - https://www.scipost.org/SciPostPhysCore.6.2.034
JF  - SciPost Physics Core
JA  - SciPost Phys. Core
VL  - 6
IS  - 2
SP  - 034
A1  - Badger, Simon
AU  - Butter, Anja
AU  - Luchmann, Michel
AU  - Pitz, Sebastian
AU  - Plehn, Tilman
AB  - Evaluating loop amplitudes is a time-consuming part of LHC event generation. For di-photon production with jets we show that simple, Bayesian networks can learn such amplitudes and model their uncertainties reliably. A boosted training of the Bayesian network further improves the uncertainty estimate and the network precision in critical phase space regions. In general, boosted network training of Bayesian networks allows us to move between fit-like and interpolation-like regimes of network training.
ER  -

@Article{10.21468/SciPostPhysCore.6.2.034,
	title={{Loop amplitudes from precision networks}},
	author={Simon Badger and Anja Butter and Michel Luchmann and Sebastian Pitz and Tilman Plehn},
	journal={SciPost Phys. Core},
	volume={6},
	pages={034},
	year={2023},
	publisher={SciPost},
	doi={10.21468/SciPostPhysCore.6.2.034},
	url={https://scipost.org/10.21468/SciPostPhysCore.6.2.034},
}

Cited by 6

Ontology / Topics

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Machine learning (ML) Monte-Carlo simulations perturbation theory

Authors / Affiliations: mappings to Contributors and Organizations

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¹ Simon Badger,
² ³ Anja Butter,
³ Michel Luchmann,
³ Sebastian Pitz,
³ Tilman Plehn

Funders for the research work leading to this publication

Deutsche Forschungsgemeinschaft / German Research FoundationDeutsche Forschungsgemeinschaft [DFG]
Horizon 2020 (through Organization: European Commission [EC])