Xsec: the cross-section evaluation code

• Published in: The European physical journal. C, Particles and fields Vol. 80; no. 12; pp. 1 - 30
• Main Authors: Buckley, Andy, Kvellestad, Anders, Raklev, Are, Scott, Pat, Sparre, Jon Vegard, Van den Abeele, Jeriek, Vazquez-Holm, Ingrid A.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2020; Springer; Springer Nature B.V; Springer Verlag (Germany); SpringerOpen
• Subjects: Analysis; Astronomy; Astrophysics and Cosmology; Cross-sections; Distribution (Probability theory); Distribution functions; Elementary Particles; Flavor (particle physics); Gaussian process; Hadrons; Heavy Ions; High Energy Physics - Phenomenology; Large Hadron Collider; Machine learning; Mathematical models; Measurement Science and Instrumentation; Nuclear Energy; Nuclear Physics; Parameters; Partons; Physics; Physics and Astronomy; Quantum Field Theories; Quantum Field Theory; Software; Software development tools; Special Article – Tools for Experiment and Theory; String Theory; Supersymmetry; supersymmetry; parton: distribution function; CERN LHC Coll; neural network; resummation; flavor; gluino: pair production; new physics: search for; squark: pair production; minimal supersymmetric standard model; hadron: colliding beams; higher-order: 1; total cross section: calculated; programming
• ISSN: 1434-6044; 1434-6052
• DOI: 10.1140/epjc/s10052-020-08635-y

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 , and often beyond, via either higher-order terms at fixed powers of α s , 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 α s . 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.