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001 | 480166 | ||
005 | 20250715175410.0 | ||
024 | 7 | _ | |a 10.1088/1748-0221/17/09/P09028 |2 doi |
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088 | _ | _ | |a arXiv:2202.07352 |2 arXiv |
100 | 1 | _ | |a Bieringer, S. |0 P:(DE-H253)PIP1096755 |b 0 |
245 | _ | _ | |a Calomplification — the power of generative calorimeter models |
260 | _ | _ | |a London |c 2022 |b Inst. of Physics |
336 | 7 | _ | |a article |2 DRIVER |
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336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1713282054_2951524 |2 PUB:(DE-HGF) |
336 | 7 | _ | |a ARTICLE |2 BibTeX |
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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 |2 INSPIRE |
650 | _ | 7 | |a calorimeter: electromagnetic |2 INSPIRE |
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650 | _ | 7 | |a Detector modelling and simulations I (interaction of radiation with matter |2 autogen |
650 | _ | 7 | |a interaction of photons with matter |2 autogen |
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650 | _ | 7 | |a etc) |2 autogen |
650 | _ | 7 | |a Simulation methods and programs |2 autogen |
650 | _ | 7 | |a Analysis and statistical methods |2 autogen |
650 | _ | 7 | |a Calorimeter methods |2 autogen |
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700 | 1 | _ | |a Eren, E. |0 P:(DE-H253)PIP1020256 |b 3 |e Corresponding author |
700 | 1 | _ | |a Gaede, F. |0 P:(DE-H253)PIP1002530 |b 4 |
700 | 1 | _ | |a Hundhausen, D. |0 P:(DE-H253)PIP1093974 |b 5 |
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773 | _ | _ | |a 10.1088/1748-0221/17/09/P09028 |g Vol. 17, no. 09, p. P09028 - |0 PERI:(DE-600)2235672-1 |n 09 |p P09028 |t Journal of Instrumentation |v 17 |y 2022 |x 1748-0221 |
787 | 0 | _ | |a Bieringer, Sebastian Guido et.al. |d 2022 |i IsParent |0 PUBDB-2022-00968 |r DESY-22-031 ; arXiv:2202.07352 |t Calomplification - The Power of Generative Calorimeter Models |
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