Exploring phase space with Nested Sampling

We present the first application of a Nested Sampling algorithm to explore the high-dimensional phase space of particle collision events. We describe the adaptation of the algorithm, designed to perform Bayesian inference computations, to the integration of partonic scattering cross sections and the...

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Bibliographic Details
Published in:arXiv.org 2022-08
Main Authors: Yallup, David, Janßen, Timo, Schumann, Steffen, Handley, Will
Format: Article
Language:English
Subjects:
Algorithms
Bayesian analysis
Gluons
Particle collisions
Sampling methods
Scattering cross sections
Statistical inference
Online Access:Get full text
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Summary:We present the first application of a Nested Sampling algorithm to explore the high-dimensional phase space of particle collision events. We describe the adaptation of the algorithm, designed to perform Bayesian inference computations, to the integration of partonic scattering cross sections and the generation of individual events distributed according to the corresponding squared matrix element. As a first concrete example we consider gluon scattering processes into 3-, 4- and 5-gluon final states and compare the performance with established sampling techniques. Starting from a flat prior distribution Nested Sampling outperforms the Vegas algorithm and achieves results comparable to a dedicated multi-channel importance sampler. We outline possible approaches to combine Nested Sampling with non-flat prior distributions to further reduce the variance of integral estimates and to increase unweighting efficiencies.
ISSN:2331-8422
DOI:10.48550/arxiv.2205.02030