GANplifying event samples

Butter, Anja; Diefenbacher, Sascha; Kasieczka, Gregor; Nachman, Benjamin; Plehn, Tilman

doi:10.21468/SciPostPhys.10.6.139

SciPost Physics

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.

TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhys.10.6.139
TI  - GANplifying event samples
PY  - 2021/06/10
UR  - https://scipost.org/SciPostPhys.10.6.139
JF  - SciPost Physics
JA  - SciPost Phys.
VL  - 10
IS  - 6
SP  - 139
A1  - Butter, Anja
AU  - Diefenbacher, Sascha
AU  - Kasieczka, Gregor
AU  - Nachman, Benjamin
AU  - Plehn, Tilman
AB  - 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.
ER  -

@Article{10.21468/SciPostPhys.10.6.139,
	title={{GANplifying event samples}},
	author={Anja Butter and Sascha Diefenbacher and Gregor Kasieczka and Benjamin Nachman and Tilman Plehn},
	journal={SciPost Phys.},
	volume={10},
	pages={139},
	year={2021},
	publisher={SciPost},
	doi={10.21468/SciPostPhys.10.6.139},
	url={https://scipost.org/10.21468/SciPostPhys.10.6.139},
}

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