Exploring phase space with Neural Importance Sampling
Enrico Bothmann, Timo Janßen, Max Knobbe, Tobias Schmale, Steffen Schumann
SciPost Phys. 8, 069 (2020) · published 29 April 2020
- doi: 10.21468/SciPostPhys.8.4.069
- Submissions/Reports
Abstract
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.
Cited by 68
Authors / Affiliation: mappings to Contributors and Organizations
See all Organizations.- 1 Enrico Bothmann,
- 1 Timo Janßen,
- 1 Max Knobbe,
- 1 Tobias Schmale,
- 1 Steffen Schumann
- Fulbright Association
- Horizon 2020 (through Organization: European Commission [EC])