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Journal Article | PUBDB-2022-03543 |
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2022
Inst. of Physics
London
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Please use a persistent id in citations: doi:10.1088/1748-0221/17/09/P09028 doi:10.3204/PUBDB-2022-03543
Report No.: DESY-22-111; arXiv:2202.07352
Abstract: 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.
Keyword(s): photon: showers ; calorimeter: electromagnetic ; costs ; Detector modelling and simulations I (interaction of radiation with matter ; interaction of photons with matter ; interaction of hadrons with matter ; etc) ; Simulation methods and programs ; Analysis and statistical methods ; Calorimeter methods
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Calomplification - The Power of Generative Calorimeter Models
[10.3204/PUBDB-2022-00968]
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