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Unweighting multijet event generation using factorisation-aware neural networks

by Timo Janßen, Daniel Maître, Steffen Schumann, Frank Siegert, Henry Truong

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Submission summary

Authors (as registered SciPost users): Timo Janßen · Steffen Schumann
Submission information
Preprint Link:  (pdf)
Date accepted: 2023-07-20
Date submitted: 2023-05-17 10:40
Submitted by: Janßen, Timo
Submitted to: SciPost Physics
Ontological classification
Academic field: Physics
  • High-Energy Physics - Experiment
  • High-Energy Physics - Phenomenology
Approaches: Computational, Phenomenological


In this article we combine a recently proposed method for factorisation-aware matrix element surrogates with an unbiased unweighting algorithm. We show that employing a sophisticated neural network emulation of QCD multijet matrix elements based on dipole factorisation can lead to a drastic acceleration of unweighted event generation. We train neural networks for a selection of partonic channels contributing at the tree-level to $Z+4,5$ jets and $t\bar{t}+3,4$ jets production at the LHC which necessitates a generalisation of the dipole emulation model to include initial state partons as well as massive final state quarks. We also present first steps towards the emulation of colour-sampled amplitudes. We incorporate these emulations as fast and accurate surrogates in a two-stage rejection sampling algorithm within the Sherpa Monte Carlo that yields unbiased unweighted events suitable for phenomenological analyses and post-processing in experimental workflows, e.g. as input to a time-consuming detector simulation. For the computational cost of unweighted events we achieve a reduction by factors between $16$ and $350$ for the considered channels.

Author comments upon resubmission

Dear Editor,

We have revised our manuscript with the requested changes. Our thanks go to the referees for their helpful comments, which improve the correctness and readability of the article. We hope that the manuscript can be published in its current form.

All the best,

the authors

List of changes

see replies to the referees

Published as SciPost Phys. 15, 107 (2023)

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