Phase space sampling with Markov Chain Monte Carlo methods

TL;DR

This paper explores using Markov Chain Monte Carlo methods combined with particle physics simulation tools to efficiently sample complex phase spaces in high-energy collider experiments.

Contribution

It introduces a novel integration of MCMC algorithms with the Sherpa event generator for high-dimensional phase space exploration.

Findings

Successful implementation of MCMC with Sherpa for collider processes

First results on Z+3 jets production at the LHC

Demonstration of efficient phase space sampling in complex scenarios

Abstract

We present a study on using Markov Chain Monte Carlo (MCMC) techniques to explore the high-dimensional and multi-modal phase space of scattering events at high-energy particle colliders. To this end, we combine the BAT.jl package that provides implementations of a variety of MCMC algorithms with the Sherpa event generator framework. We discuss technical aspects of the implementation and the resulting algorithm and present first results for the process of $Z+3$ jets production at the LHC.