A fast and accurate method for perturbative resummation of transverse momentum-dependent observables

TL;DR

This paper introduces a new method for perturbative resummation of transverse momentum-dependent observables, improving accuracy and computational speed by avoiding Landau pole issues and providing semi-analytic formulas.

Contribution

A novel scheme for TMD resummation in momentum space that enhances convergence and computational efficiency, applicable to $q_T$ spectra in collider physics.

Findings

Achieved NNLL accuracy for Drell-Yan and Higgs production.

Developed a semi-analytic formula for resummed cross sections.

Ensured convergence of the $b$ integral without Landau pole issues.

Abstract

We propose a novel strategy for the perturbative resummation of transverse momentum-dependent (TMD) observables, using the $q_T$ spectra of gauge bosons ($\gamma^*$, Higgs) in $pp$ collisions in the regime of low (but perturbative) transverse momentum $q_T$ as a specific example. First we introduce a scheme to choose the factorization scale for virtuality in momentum space instead of in impact parameter space, allowing us to avoid integrating over (or cutting off) a Landau pole in the inverse Fourier transform of the latter to the former. The factorization scale for rapidity is still chosen as a function of impact parameter $b$, but in such a way designed to obtain a Gaussian form (in $\ln b$) for the exponentiated rapidity evolution kernel, guaranteeing convergence of the $b$ integral. We then apply this scheme to obtain the $q_T$ spectra for Drell-Yan and Higgs production at NNLL accuracy. In addition, using this scheme we are able to obtain a fast semi-analytic formula for the perturbative resummed cross sections in momentum space: analytic in its dependence on all physical variables at each order of logarithmic accuracy, up to a numerical expansion for the pure mathematical Bessel function in the inverse Fourier transform that needs to be performed just once for all observables and kinematics, to any desired accuracy.