Phase space sampling with Markov Chain Monte Carlo methods

The efficient exploration of the high-dimensional and multi-modal phase space of scattering events at high-energy particle colliders presents a severe computational challenge. We here discuss the application of Markov Chain Monte Carlo (MCMC) techniques, an approach that so far received rather little attention for this purpose. 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.

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Publication Details

Journal
EPJ Web of Conferences
Published
2025-01-01
DOI
https://doi.org/10.1051/epjconf/202533701319
Citations
1
Primary Topic
Markov Chains and Monte Carlo Methods
Type
article
Field-Weighted Citation Impact
2.63

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Phase space sampling with Markov Chain Monte Carlo methods

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Phase space sampling with Markov Chain Monte Carlo methods

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article en
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Abstract

The efficient exploration of the high-dimensional and multi-modal phase space of scattering events at high-energy particle colliders presents a severe computational challenge. We here discuss the application of Markov Chain Monte Carlo (MCMC) techniques, an approach that so far received rather little attention for this purpose. 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.

EPJ Web of ConferencesVol. 337
TU Dortmund University (DE), University of Göttingen (DE)
Bundesministerium für Bildung und Forschung
Openalex Percentile: Top 8%
Markov Chains and Monte Carlo Methods
2.63
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