Bieringer, S.
[Author];
Butter, A.
[Author];
Diefenbacher, S.
[Author];
Eren, E.
[Author];
Gaede, F.
[Author];
Hundhausen, D.
[Author];
Kasieczka, G.
[Author];
Nachman, B.
[Author];
Plehn, T.
[Author];
Trabs, M.
[Author]
Calomplification — the power of generative calorimeter models
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Media type:
Text;
E-Article
Title:
Calomplification — the power of generative calorimeter models
Contributor:
Bieringer, S.
[Author];
Butter, A.
[Author];
Diefenbacher, S.
[Author];
Eren, E.
[Author];
Gaede, F.
[Author];
Hundhausen, D.
[Author];
Kasieczka, G.
[Author];
Nachman, B.
[Author];
Plehn, T.
[Author];
Trabs, M.
[Author]
Published:
Institute of Physics Publishing Ltd, 2022-10-24
Published in:Journal of Instrumentation, 17 (09), Art.Nr. P09028 ; ISSN: 1748-0221
Footnote:
Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
Description:
Motivated by the high computational costs of classical simulations, machine-learned generative models can be extremely useful in particle physics and elsewhere. They become especially attractive when surrogate models can efficiently learn the underlying distribution, such that a generated sample outperforms a training sample of limited size. This kind of GANplification has been observed for simple Gaussian models. We show the same effect for a physics simulation, specifically photon showers in an electromagnetic calorimeter.