Autor: |
Timo Janßen, Daniel Maître, Steffen Schumann, Frank Siegert, Henry Truong |
Jazyk: |
angličtina |
Rok vydání: |
2023 |
Předmět: |
|
Zdroj: |
SciPost Physics, Vol 15, Iss 3, p 107 (2023) |
Druh dokumentu: |
article |
ISSN: |
2542-4653 |
DOI: |
10.21468/SciPostPhys.15.3.107 |
Popis: |
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. |
Databáze: |
Directory of Open Access Journals |
Externí odkaz: |
|