Towards precise collider predictions: the Parton Branching method

TL;DR

The paper introduces the Parton Branching method, a Monte Carlo approach based on TMD factorization, to improve collider predictions by combining resummation with NLO calculations, demonstrated on Drell-Yan processes.

Contribution

It presents a novel Monte Carlo method for TMD evolution that integrates NLO matching, enhancing the accuracy of collider predictions involving multiple energy scales.

Findings

Successful application to Drell-Yan measurements across various energies

Effective matching of NLO TMD evolution with MC calculations

Improved theoretical predictions for collider observables

Abstract

The collinear factorization theorem, combined with finite-order calculations in perturbative QCD, provides a powerful framework to obtain predictions for many collider observables. However, for observables which involve multiple energy scales, transverse degrees of freedom cannot be neglected, and finite-order perturbative calculations have to be combined with resummed calculations to all orders in the QCD running coupling in order to obtain reliable theoretical predictions, capable of describing experimental measurements. This is traditionally done either by analytic resummation methods or by parton shower (PS) Monte Carlo (MC) methods. In this talk we present the Parton Branching (PB) MC method to obtain QCD collider predictions based on Transverse Momentum Dependent (TMD) factorization. The PB provides evolution equations for TMD Parton Distribution Functions (PDFs) which, upon fitting TMD PDFs to experimental data, can be used in TMD MC event generators. We present the basic concepts of the method and illustrate its applications to collider measurements focusing on Drell-Yan (DY) lepton-pair production in different kinematic ranges, from fixed-target to LHC energies. We discuss the latest developments of the method concentrating especially on the matching of next-to-leading-order (NLO) TMD evolution with MC-at-NLO calculations of NLO matrix elements.