GANplifying event samples
Anja Butter, Sascha Diefenbacher, Gregor Kasieczka, Benjamin Nachman, Tilman Plehn
SciPost Phys. 10, 139 (2021) · published 10 June 2021
- doi: 10.21468/SciPostPhys.10.6.139
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Abstract
A critical question concerning generative networks applied to event generation in particle physics is if the generated events add statistical precision beyond the training sample. We show for a simple example with increasing dimensionality how generative networks indeed amplify the training statistics. We quantify their impact through an amplification factor or equivalent numbers of sampled events.
Cited by 43
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Authors / Affiliations: mappings to Contributors and Organizations
See all Organizations.- 1 Ruprecht-Karls-Universität Heidelberg / Heidelberg University
- 2 Universität Hamburg / University of Hamburg [UH]
- 3 Lawrence Berkeley National Laboratory [LBNL]
Funders for the research work leading to this publication