2020/06/30 by Andy Buckley, Anders Kvellestad, Are Raklev +4
Physics and Astronomy · #Code (set theory) #Component (thermodynamics) #Computation #Coupling (piping) #Focus (optics) #Fraction (chemistry) #High-Energy Particle Collisions Research #Particle physics theoretical and experimental studies #Process (computing) #Quantum Chromodynamics and Particle Interactions #Sample (material) #Software #hep-ex #hep-ph
paper · pdf · doi:10.1140/epjc/s10052-020-08635-y
published as Eur. Phys. J. C 80, 1106 (2020) · Accepted version
openalex created_date 2020/07/10 · openalex publication_date 2020/12/01 · arxiv created 2020/12/18 · arxiv updated 2020/12/29 · openalex updated_date 2026/08/05
Abstract 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 α s <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>α</mml:mi> <mml:mi>s</mml:mi> </mml:msub> </mml:math> , and often beyond, via either higher-order terms at fixed powers of α s <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>α</mml:mi> <mml:mi>s</mml:mi> </mml:msub> </mml:math> , 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 , 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 α s <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>α</mml:mi> <mml:mi>s</mml:mi> </mml:msub> </mml:math> . 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.