Calomplification: the power of generative calorimeter models
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 out...
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| Hauptverfasser: | , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) Kapitel/Artikel |
| Sprache: | Englisch |
| Veröffentlicht: |
9 May 2022
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| In: |
Arxiv
Year: 2022, Pages: 1-17 |
| DOI: | 10.48550/arXiv.2202.07352 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.48550/arXiv.2202.07352 Verlag, lizenzpflichtig, Volltext: http://arxiv.org/abs/2202.07352 
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| Verfasserangaben: | Sebastian Bieringer, Anja Butter, Sascha Diefenbacher, Engin Eren, Frank Gaede, Daniel Hundhausen, Gregor Kasieczka, Benjamin Nachman, Tilman Plehn, and Mathias Trabs |
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| 520 | |a 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. | ||
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