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• W4318907208 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 <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML display=inline><mml:mrow><mml:mi>Z</mml:mi><mml:mo>+</mml:mo><mml:mn>4</mml:mn><mml:mo>,</mml:mo><mml:mn>5</mml:mn></mml:mrow></mml:math> jets and tbar{t}+3,4 <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML display=inline><mml:mrow><mml:mi>t</mml:mi><mml:mover><mml:mi>t</mml:mi><mml:mo accent=true>‾</mml:mo></mml:mover><mml:mo>+</mml:mo><mml:mn>3</mml:mn><mml:mo>,</mml:mo><mml:mn>4</mml:mn></mml:mrow></mml:math> 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." @default.
• W4318907208 created "2023-02-03" @default.
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• W4318907208 date "2023-09-21" @default.
• W4318907208 modified "2023-10-18" @default.
• W4318907208 title "Unweighting multijet event generation using factorisation-aware neural networks" @default.
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• W4318907208 doi "https://doi.org/10.21468/scipostphys.15.3.107" @default.
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