Special Article – Tools for Experiment and Theory
Xsec: the cross-section evaluation code
School of Physics and Astronomy, University of Glasgow, G12 8QQ, Glasgow, UK
2 Department of Physics, Imperial College London, South Kensington, SW7 2AZ, London, UK
3 Department of Physics, University of Oslo, 0316, Oslo, Norway
4 School of Mathematics and Physics, The University of Queensland, St. Lucia, 4072, Brisbane, QLD, Australia
5 The Norwegian Labour and Welfare Administration, 0557, Oslo, Norway
6 Institut de Physique Théorique, Université Paris-Saclay, CEA, CNRS, 91191, Gif-sur-Yvette, France
Accepted: 2 November 2020
Published online: 2 December 2020
The evaluation of higher-order cross-sections is an important component in the search for new physics, both at hadron colliders and elsewhere. For most new physics processes of interest, total cross-sections are known at next-to-leading order (NLO) in the strong coupling , and often beyond, via either higher-order terms at fixed powers of , or multi-emission resummation. However, the computation time for such higher-order cross-sections is prohibitively expensive, and precludes efficient evaluation in parameter-space scans beyond two dimensions. Here we describe the software tool xsec, which allows for fast evaluation of cross-sections based on the use of machine-learning regression, using distributed Gaussian processes trained on a pre-generated sample of parameter points. This first version of the code provides all NLO Minimal Supersymmetric Standard Model strong-production cross-sections at the LHC, for individual flavour final states, evaluated in a fraction of a second. Moreover, it calculates regression errors, as well as estimates of errors from higher-order contributions, from uncertainties in the parton distribution functions, and from the value of . While we focus on a specific phenomenological model of supersymmetry, the method readily generalises to any process where it is possible to generate a sufficient training sample.
© The Author(s) 2020
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