Calculation for Non-global Logarithms with Neural Networks

21 Jul 2020, 17:00
9m
Online

Online

Abstract for poster-session Measurements and Calculations Session 6

Description

High-precision all-order calculations can only be performed for a narrow class of observables, which are sensitive to radiation over the entire final state phase-space. When phase-space boundaries are introduced, the resummation is affected by so-called non-global logarithms, which have an intricate all-order structure. In this talk, we present a first-principle calculation for the non-global logarithms, and some improvements for higher-order calculation and resummation are proposed with artificial neural networks, which can dramatically speed up needed theory calculations.

Authors

Mr Chang Wu (Università di Genova & INFN Genova) Simone Marzani (Università di Genova and INFN Genova) Michael Spannowsky (University of Durham (GB))

Presentation materials