DeepXS: fast approximation of MSSM electroweak cross sections at NLO

Autor: Sydney Otten, Krzysztof Rolbiecki, Sascha Caron, Jong-Soo Kim, Roberto Ruiz de Austri, Jamie Tattersall
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: European Physical Journal C: Particles and Fields, Vol 80, Iss 1, Pp 1-9 (2020)
Druh dokumentu: article
ISSN: 1434-6044
1434-6052
DOI: 10.1140/epjc/s10052-019-7562-1
Popis: Abstract We present a deep learning solution to the prediction of particle production cross sections over a complicated, high-dimensional parameter space. We demonstrate the applicability by providing state-of-the-art predictions for the production of charginos and neutralinos at the Large Hadron Collider (LHC) at the next-to-leading order in the phenomenological MSSM-19 and explicitly demonstrate the performance for $$pp\rightarrow \tilde{\chi }^+_1\tilde{\chi }^-_1,$$ pp→χ~1+χ~1-, $$\tilde{\chi }^0_2\tilde{\chi }^0_2$$ χ~20χ~20 and $$\tilde{\chi }^0_2\tilde{\chi }^\pm _1$$ χ~20χ~1± as a proof of concept which will be extended to all SUSY electroweak pairs. We obtain errors that are lower than the uncertainty from scale and parton distribution functions with mean absolute percentage errors of well below $$0.5\,\%$$ 0.5% allowing a safe inference at the next-to-leading order with inference times that improve the Monte Carlo integration procedures that have been available so far by a factor of $$\mathscr {O}(10^7)$$ O(107) from $$\mathscr {O}(\mathrm{min})$$ O(min) to $$\mathscr {O}(\mu \mathrm{s})$$ O(μs) per evaluation.
Databáze: Directory of Open Access Journals
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