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| 001 | 474749 | ||
| 005 | 20230510115614.0 | ||
| 024 | 7 | _ | |2 INSPIRETeX |a Bieringer:2022cbs |
| 024 | 7 | _ | |2 inspire |a inspire:2032657 |
| 024 | 7 | _ | |2 arXiv |a arXiv:2202.07352 |
| 024 | 7 | _ | |2 datacite_doi |a 10.3204/PUBDB-2022-00968 |
| 037 | _ | _ | |a PUBDB-2022-00968 |
| 041 | _ | _ | |a English |
| 088 | _ | _ | |2 DESY |a DESY-22-031 |
| 088 | _ | _ | |2 arXiv |a arXiv:2202.07352 |
| 100 | 1 | _ | |0 P:(DE-H253)PIP1096755 |a Bieringer, Sebastian Guido |b 0 |e Corresponding author |
| 245 | _ | _ | |a Calomplification - The Power of Generative Calorimeter Models |
| 260 | _ | _ | |c 2022 |
| 336 | 7 | _ | |0 PUB:(DE-HGF)25 |2 PUB:(DE-HGF) |a Preprint |b preprint |m preprint |s 1646213843_9987 |
| 336 | 7 | _ | |2 ORCID |a WORKING_PAPER |
| 336 | 7 | _ | |0 28 |2 EndNote |a Electronic Article |
| 336 | 7 | _ | |2 DRIVER |a preprint |
| 336 | 7 | _ | |2 BibTeX |a ARTICLE |
| 336 | 7 | _ | |2 DataCite |a Output Types/Working Paper |
| 500 | _ | _ | |a 17 pages, 10 figures |
| 520 | _ | _ | |a Motivated by the high computational costs of classical simulations, machine-learned gen- erative models can be extremely useful in particle physics and elsewhere. They become especially attractive when surrogate models can efficiently learn the underlying distri- bution, 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 a highly-granular electromagnetic calorimeter. |
| 536 | _ | _ | |0 G:(DE-HGF)POF4-623 |a 623 - Data Management and Analysis (POF4-623) |c POF4-623 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to INSPIRE |
| 650 | _ | 7 | |2 INSPIRE |a photon, showers |
| 650 | _ | 7 | |2 INSPIRE |a calorimeter, electromagnetic |
| 650 | _ | 7 | |2 INSPIRE |a costs |
| 693 | _ | _ | |0 EXP:(DE-MLZ)NOSPEC-20140101 |5 EXP:(DE-MLZ)NOSPEC-20140101 |e No specific instrument |x 0 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Anja Butter |b 1 |
| 700 | 1 | _ | |0 P:(DE-H253)PIP1090777 |a Diefenbacher, Sascha Daniel |b 2 |
| 700 | 1 | _ | |0 P:(DE-H253)PIP1020256 |a Eren, Engin |b 3 |
| 700 | 1 | _ | |0 P:(DE-H253)PIP1002530 |a Gaede, Frank |b 4 |
| 700 | 1 | _ | |0 P:(DE-H253)PIP1093974 |a Hundhausen, Daniel Christian |b 5 |
| 700 | 1 | _ | |0 P:(DE-H253)PIP1081743 |a Kasieczka, Gregor |b 6 |
| 700 | 1 | _ | |0 P:(DE-H253)PIP1095640 |a Nachman, Benjamin |b 7 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Plehn, Tilman |b 8 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Mathias Trabs, KIT |b 9 |
| 856 | 4 | _ | |u https://bib-pubdb1.desy.de/record/474749/files/HTML-Approval_of_scientific_publication.html |
| 856 | 4 | _ | |u https://bib-pubdb1.desy.de/record/474749/files/PDF-Approval_of_scientific_publication.pdf |
| 856 | 4 | _ | |u https://bib-pubdb1.desy.de/record/474749/files/2202.02292v1.pdf |y OpenAccess |
| 856 | 4 | _ | |u https://bib-pubdb1.desy.de/record/474749/files/2202.02292v1.pdf?subformat=pdfa |x pdfa |y OpenAccess |
| 909 | C | O | |o oai:bib-pubdb1.desy.de:474749 |p openaire |p open_access |p VDB |p driver |p dnbdelivery |
| 910 | 1 | _ | |0 I:(DE-HGF)0 |6 P:(DE-H253)PIP1096755 |a External Institute |b 0 |k Extern |
| 910 | 1 | _ | |0 I:(DE-HGF)0 |6 P:(DE-HGF)0 |a Institut für Theoretische Physik, Universität Heidelberg |b 1 |
| 910 | 1 | _ | |0 I:(DE-HGF)0 |6 P:(DE-H253)PIP1090777 |a External Institute |b 2 |k Extern |
| 910 | 1 | _ | |0 I:(DE-588b)2008985-5 |6 P:(DE-H253)PIP1020256 |a Deutsches Elektronen-Synchrotron |b 3 |k DESY |
| 910 | 1 | _ | |0 I:(DE-588b)2008985-5 |6 P:(DE-H253)PIP1002530 |a Deutsches Elektronen-Synchrotron |b 4 |k DESY |
| 910 | 1 | _ | |0 I:(DE-HGF)0 |6 P:(DE-H253)PIP1093974 |a External Institute |b 5 |k Extern |
| 910 | 1 | _ | |0 I:(DE-HGF)0 |6 P:(DE-H253)PIP1081743 |a External Institute |b 6 |k Extern |
| 910 | 1 | _ | |0 I:(DE-HGF)0 |6 P:(DE-H253)PIP1095640 |a External Institute |b 7 |k Extern |
| 910 | 1 | _ | |0 I:(DE-HGF)0 |6 P:(DE-HGF)0 |a Institut für Theoretische Physik, Universität Heidelberg |b 8 |
| 913 | 1 | _ | |0 G:(DE-HGF)POF4-623 |1 G:(DE-HGF)POF4-620 |2 G:(DE-HGF)POF4-600 |3 G:(DE-HGF)POF4 |4 G:(DE-HGF)POF |a DE-HGF |b Forschungsbereich Materie |l Matter and Technologies |v Data Management and Analysis |x 0 |
| 914 | 1 | _ | |y 2022 |
| 915 | _ | _ | |0 StatID:(DE-HGF)0510 |2 StatID |a OpenAccess |
| 915 | _ | _ | |a Published |0 StatID:(DE-HGF)0580 |2 StatID |
| 920 | 1 | _ | |0 I:(DE-H253)FTX-20210408 |k FTX |l Technol. zukünft. Teilchenph. Experim. |x 0 |
| 980 | _ | _ | |a preprint |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-H253)FTX-20210408 |
| 980 | 1 | _ | |a FullTexts |
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