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024 7 _ |a 10.1088/1748-0221/17/09/P09028
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100 1 _ |a Bieringer, S.
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245 _ _ |a Calomplification — the power of generative calorimeter models
260 _ _ |a London
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500 _ _ |a 17 pages, 10 figures
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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650 _ 7 |a photon: showers
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650 _ 7 |a calorimeter: electromagnetic
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650 _ 7 |a Detector modelling and simulations I (interaction of radiation with matter
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650 _ 7 |a interaction of photons with matter
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650 _ 7 |a interaction of hadrons with matter
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650 _ 7 |a etc)
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650 _ 7 |a Simulation methods and programs
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650 _ 7 |a Analysis and statistical methods
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650 _ 7 |a Calorimeter methods
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700 1 _ |a Butter, A.
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700 1 _ |a Diefenbacher, S.
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700 1 _ |a Eren, E.
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700 1 _ |a Gaede, F.
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700 1 _ |a Hundhausen, D.
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700 1 _ |a Kasieczka, G.
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700 1 _ |a Nachman, B.
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773 _ _ |a 10.1088/1748-0221/17/09/P09028
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787 0 _ |a Bieringer, Sebastian Guido et.al.
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|t Calomplification - The Power of Generative Calorimeter Models
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