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Exploring phase space with Neural Importance Sampling

by Enrico Bothmann, Timo Janßen, Max Knobbe, Tobias Schmale, Steffen Schumann

Submission summary

As Contributors: Timo Janßen · Steffen Schumann
Arxiv Link:
Date submitted: 2020-01-30
Submitted by: Schumann, Steffen
Submitted to: SciPost Physics
Discipline: Physics
Subject area: High-Energy Physics - Phenomenology
Approaches: Theoretical, Computational, Phenomenological


We present a novel approach for the integration of scattering cross sections and the generation of partonic event samples in high-energy physics. We propose an importance sampling technique capable of overcoming typical deficiencies of existing approaches by incorporating neural networks. The method guarantees full phase space coverage and the exact reproduction of the desired target distribution, in our case given by the squared transition matrix element. We study the performance of the algorithm for a few representative examples, including top-quark pair production and gluon scattering into three- and four-gluon final states.

Current status:
Editor-in-charge assigned

Submission & Refereeing History

Submission 2001.05478v2 on 30 January 2020

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