# Efficient Fourier Transforms for Transverse Momentum Dependent Distributions

## TL;DR

This paper introduces a fast, accurate numerical Hankel transform algorithm for TMD calculations in particle physics, improving computational efficiency and precision across various processes.

## Contribution

The paper develops and provides open-source implementations of a novel Hankel transform algorithm tailored for TMD phenomenology, enhancing numerical accuracy and speed.

## Key findings

- Improved numerical accuracy in TMD calculations.
- Open-source libraries in multiple programming languages.
- Enhanced computational efficiency for Fourier transforms.

## Abstract

Hadron production at low transverse momenta in semi-inclusive deep inelastic scattering can be described by transverse momentum dependent (TMD) factorization. This formalism has also been widely used to study the Drell-Yan process and back-to-back hadron pair production in $e^+e^-$ collisions. These processes are the main ones for extractions of TMD parton distribution functions and TMD fragmentation functions, which encode important information about nucleon structure and hadronization. One of the most widely used TMD factorization formalism in phenomenology formulates TMD observables in coordinate $b_\perp$-space, the conjugate space of the transverse momentum. The Fourier transform from $b_\perp$-space back into transverse momentum space is sufficiently complicated due to oscillatory integrands that it requires a careful and computationally intensive numerical treatment in order to avoid potentially large numerical errors. Within the TMD formalism, the azimuthal angular dependence is analytically integrated and the two-dimensional $b_\perp$ integration reduces to a one-dimensional integration over the magnitude $b_\perp$. In this paper we develop a fast numerical Hankel transform algorithm for such a $b_\perp$-integration that improves the numerical accuracy of TMD calculations in all standard processes. Libraries for this algorithm are implemented in Python 2.7 and 3, C++, as well as FORTRAN77. All packages are made available open source.