Unweighting multijet event generation using factorisation-aware neural networks
Timo Janßen, Daniel Maître, Steffen Schumann, Frank Siegert, Henry Truong
SciPost Phys. 15, 107 (2023) · published 21 September 2023
- doi: 10.21468/SciPostPhys.15.3.107
- Submissions/Reports
Abstract
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.
Cited by 6
Authors / Affiliations: mappings to Contributors and Organizations
See all Organizations.- 1 Timo Janßen,
- 2 Daniel Maître,
- 1 Steffen Schumann,
- 3 Frank Siegert,
- 2 Henry Truong
- 1 Georg-August-Universität Göttingen / University of Göttingen [GAU]
- 2 Durham University
- 3 Technische Universität Dresden / Dresden University of Technology [TUD]