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arXiv · 2301.13562

Unweighting multijet event generation using factorisation-aware neural networks

Timo Janßen·Daniel Maître·Steffen Schumann·Frank Siegert·Henry Truong
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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.

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Timo Janßen, Daniel Maître, Steffen Schumann, Frank Siegert, Henry Truong. 2023-05-16. Unweighting multijet event generation using factorisation-aware neural networks. https://doi.org/10.21468/scipostphys.15.3.107

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