Unweighting multijet event generation using factorisation-aware neural networks
2023 | journal article. A publication with affiliation to the University of Göttingen.
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- Authors
- Janßen, Timo; Maître, Daniel; Schumann, Steffen; Siegert, Frank; Truong, Henry
- 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 Z + 4 , 5 jets and t\bar{t}+3,4 t 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.
- Issue Date
- 2023
- Journal
- SciPost Physics
- eISSN
- 2542-4653
- Sponsor
- Bundesministerium für Bildung und Forschung http://dx.doi.org/10.13039/501100002347
Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659
Science and Technology Facilities Council http://dx.doi.org/10.13039/501100000271