000481363 001__ 481363
000481363 005__ 20220810211447.0
000481363 037__ $$aPUBDB-2022-04336
000481363 041__ $$aEnglish
000481363 1001_ $$0P:(DE-H253)PIP1088880$$aScham, Moritz$$b0$$eCorresponding author
000481363 1112_ $$aCenter for Data and Computing in Natural Sciences (CDCS) Symposium$$cHamburg$$d2022-04-26 - 2022-04-28$$gCDCS2022$$wGermany
000481363 245__ $$aGenerative modeling with Graph Neural Networks for the CMS HGCal
000481363 260__ $$c2022
000481363 3367_ $$033$$2EndNote$$aConference Paper
000481363 3367_ $$2BibTeX$$aINPROCEEDINGS
000481363 3367_ $$2DRIVER$$aconferenceObject
000481363 3367_ $$2ORCID$$aCONFERENCE_POSTER
000481363 3367_ $$2DataCite$$aOutput Types/Conference Poster
000481363 3367_ $$0PUB:(DE-HGF)24$$2PUB:(DE-HGF)$$aPoster$$bposter$$mposter$$s1660136150_1173
000481363 520__ $$aIn high energy physics, detailed and time-consuming simulations are used for particle interactions with detectors. For the upcoming High-Luminosity phase of the Large Hadron Collider (HL-LHC), the computational costs of conventional simulation tools exceeds the projected computational resources. Generative machine learning is expected to provide a fast and accurate alternative. The CMS experiment at the LHC will use a new High Granularity Calorimeter (HGCal) to cope with the high particle density. The new HGCal is an imaging calorimeter with a complex geometry and more than 3 million cells. We report on the development of a GraphGAN to simulate particle showers under these challenging conditions.
000481363 536__ $$0G:(DE-HGF)POF4-611$$a611 - Fundamental Particles and Forces (POF4-611)$$cPOF4-611$$fPOF IV$$x0
000481363 693__ $$0EXP:(DE-H253)LHC-Exp-CMS-20150101$$1EXP:(DE-588)4398783-7$$5EXP:(DE-H253)LHC-Exp-CMS-20150101$$aLHC$$eLHC: CMS$$x0
000481363 7001_ $$0P:(DE-H253)PIP1094654$$aBhattacharya, Soham$$b1$$udesy
000481363 7001_ $$0P:(DE-H253)PIP1002900$$aBorras, Kerstin$$b2$$udesy
000481363 7001_ $$aBein, Sam$$b3
000481363 7001_ $$aEren, Engin$$b4
000481363 7001_ $$aGaede, Frank$$b5
000481363 7001_ $$aKasieczka, Gregor$$b6
000481363 7001_ $$aKorcari, William$$b7
000481363 7001_ $$0P:(DE-H253)PIP1005319$$aKrücker, Dirk$$b8$$udesy
000481363 7001_ $$aMcKeown, Peter$$b9
000481363 7001_ $$0P:(DE-HGF)0$$aCMS Collaboration$$b10$$eCollaboration author
000481363 8564_ $$uhttps://indico.desy.de/event/31214/contributions/120857/
000481363 8564_ $$uhttps://bib-pubdb1.desy.de/record/481363/files/HelmholtzAI-VC21-DeGeSim.pdf$$yRestricted
000481363 8564_ $$uhttps://bib-pubdb1.desy.de/record/481363/files/HelmholtzAI-VC21-DeGeSim.pdf?subformat=pdfa$$xpdfa$$yRestricted
000481363 909CO $$ooai:bib-pubdb1.desy.de:481363$$pVDB
000481363 9101_ $$0I:(DE-588b)2008985-5$$6P:(DE-H253)PIP1088880$$aDeutsches Elektronen-Synchrotron$$b0$$kDESY
000481363 9101_ $$0I:(DE-588b)2008985-5$$6P:(DE-H253)PIP1094654$$aDeutsches Elektronen-Synchrotron$$b1$$kDESY
000481363 9101_ $$0I:(DE-588b)2008985-5$$6P:(DE-H253)PIP1002900$$aDeutsches Elektronen-Synchrotron$$b2$$kDESY
000481363 9101_ $$0I:(DE-588b)2008985-5$$6P:(DE-H253)PIP1005319$$aDeutsches Elektronen-Synchrotron$$b8$$kDESY
000481363 9101_ $$0I:(DE-588b)2008985-5$$6P:(DE-HGF)0$$aDeutsches Elektronen-Synchrotron$$b10$$kDESY
000481363 9131_ $$0G:(DE-HGF)POF4-611$$1G:(DE-HGF)POF4-610$$2G:(DE-HGF)POF4-600$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bForschungsbereich Materie$$lMatter and the Universe$$vFundamental Particles and Forces$$x0
000481363 9141_ $$y2022
000481363 9201_ $$0I:(DE-H253)CMS-20120731$$kCMS$$lLHC/CMS Experiment$$x0
000481363 980__ $$aposter
000481363 980__ $$aVDB
000481363 980__ $$aI:(DE-H253)CMS-20120731
000481363 980__ $$aUNRESTRICTED