$\text{Xsec}$: the cross-section evaluation code

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 ${\alpha }_s$, and often beyond, via either higher-order terms at fixed powers of ${\alpha }_s$, 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 $\text{xsec}$, 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 ${\alpha }_s$. 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.

arXiv commentAccepted version

References in corpus (26)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• SOFTSUSY: a program for calculating supersymmetric spectra
• SUSY Les Houches Accord: Interfacing SUSY Spectrum Calculators, Decay Packages, and Event Generators
• Stop Production at Hadron Colliders
• The Production of Charginos/Neutralinos and Sleptons at Hadron Colliders
• Threshold resummation for squark-antisquark and gluino-pair production at the LHC
• Squark and gluino hadroproduction
• Soft gluon resummation for the production of gluino-gluino and squark-antisquark pairs at the LHC
• Soft-gluon resummation for squark and gluino hadroproduction
• Precision predictions for electroweak superpartner production at hadron colliders with Resummino
• Supersymmetric top and bottom squark production at hadron colliders
• Gaugino production in proton-proton collisions at a center-of-mass energy of 8 TeV
• NNLL-fast: predictions for coloured supersymmetric particle production at the LHC with threshold and Coulomb resummation
• Threshold resummation for gaugino pair production at hadron colliders
• NNLL resummation for squark and gluino production at the LHC
• NNLL resummation for squark-antisquark pair production at the LHC
• Threshold Resummation for Slepton-Pair Production at Hadron Colliders
• NNLL resummation for stop pair-production at the LHC
• Towards NNLL resummation: hard matching coefficients for squark and gluino hadroproduction
• Joint Resummation for Gaugino Pair Production at Hadron Colliders
• Joint resummation for slepton pair production at hadron colliders
• Automated Squark and Gluino Production to Next-to-Leading Order
• NLO+NLL squark and gluino production cross-sections with threshold-improved parton distributions
• Transverse-Momentum Resummation for Slepton-Pair Production at the LHC
• Transverse-momentum resummation for gaugino-pair production at hadron colliders
• Soft gluon resummation for associated gluino-gaugino production at the LHC

Cited by in corpus (6)

• Accelerating Monte Carlo event generation -- rejection sampling using neural network event-weight estimates
• A factorisation-aware Matrix element emulator
• Optimising simulations for diphoton production at hadron colliders using amplitude neural networks
• Les Houches 2021: Physics at TeV Colliders: Report on the Standard Model Precision Wishlist
• Comparing Machine Learning and Interpolation Methods for Loop-Level Calculations
• A Novel Implementation of the Matrix Element Method at Next-to-Leading Order for the Measurement of the Higgs Self-Coupling ${\lambda }_{3H}$