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000480166 245__ $$aCalomplification — the power of generative calorimeter models
000480166 260__ $$aLondon$$bInst. of Physics$$c2022
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000480166 520__ $$aMotivated by the high computational costs of classical simulations, machine-learned generative models can be extremely useful in particle physics and elsewhere. They become especially attractive when surrogate models can efficiently learn the underlying distribution, such that a generated sample outperforms a training sample of limited size. This kind of GANplification has been observed for simple Gaussian models. We show the same effect for a physics simulation, specifically photon showers in an electromagnetic calorimeter.
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000480166 650_7 $$2INSPIRE$$aphoton: showers
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000480166 650_7 $$2autogen$$aDetector modelling and simulations I (interaction of radiation with matter
000480166 650_7 $$2autogen$$ainteraction of photons with matter
000480166 650_7 $$2autogen$$ainteraction of hadrons with matter
000480166 650_7 $$2autogen$$aetc)
000480166 650_7 $$2autogen$$aSimulation methods and programs
000480166 650_7 $$2autogen$$aAnalysis and statistical methods
000480166 650_7 $$2autogen$$aCalorimeter methods
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000480166 7001_ $$0P:(DE-H253)PIP1030245$$aButter, A.$$b1
000480166 7001_ $$0P:(DE-H253)PIP1090777$$aDiefenbacher, S.$$b2
000480166 7001_ $$0P:(DE-H253)PIP1020256$$aEren, E.$$b3$$eCorresponding author
000480166 7001_ $$0P:(DE-H253)PIP1002530$$aGaede, F.$$b4
000480166 7001_ $$0P:(DE-H253)PIP1093974$$aHundhausen, D.$$b5
000480166 7001_ $$0P:(DE-H253)PIP1081743$$aKasieczka, G.$$b6
000480166 7001_ $$0P:(DE-H253)PIP1095640$$aNachman, B.$$b7
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000480166 773__ $$0PERI:(DE-600)2235672-1$$a10.1088/1748-0221/17/09/P09028$$gVol. 17, no. 09, p. P09028 -$$n09$$pP09028$$tJournal of Instrumentation$$v17$$x1748-0221$$y2022
000480166 7870_ $$0PUBDB-2022-00968$$aBieringer, Sebastian Guido et.al.$$d2022$$iIsParent$$rDESY-22-031 ; arXiv:2202.07352$$tCalomplification - The Power of Generative Calorimeter Models
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