ExSample -- A Library for Sampling Sudakov-Type Distributions

TL;DR

ExSample is a C++ library that adaptively samples Sudakov-type distributions, crucial for radiation generation in parton showers and NLO matching, even when kernels are only numerically known.

Contribution

The paper introduces ExSample, a novel C++ library enabling adaptive sampling of complex Sudakov-type distributions with multiple dependencies.

Findings

Efficient sampling of numerically known splitting kernels.

Supports multiple degrees of freedom in distributions.

Flexible parameter handling for event-specific configurations.

Abstract

Sudakov-type distributions are at the heart of generating radiation in parton showers as well as contemporary NLO matching algorithms along the lines of the POWHEG algorithm. In this paper, the C++ library ExSample is introduced, which implements adaptive sampling of Sudakov-type distributions for splitting kernels which are in general only known numerically. Besides the evolution variable, the splitting kernels can depend on an arbitrary number of other degrees of freedom to be sampled, and any number of further parameters which are fixed on an event-by-event basis.