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000905718 1001_ $$0P:(DE-Juel1)187009$$aScham, Moritz$$b0$$eCorresponding author$$ufzj
000905718 1112_ $$aHelmholtz AI virtual conference 2021$$conline$$d2021-04-14 - 2021-04-15$$wGermany
000905718 245__ $$aDeGeSim and the High Granularity Calorimeter for the CMS Experiment at the Large Hadron Collider
000905718 260__ $$c2021
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000905718 520__ $$aWe present the beginning work on fast simulations for the new High Granularity Calorimeter (HGCal), which will become part of the CMS experiment at the Large Hadron Colider (LHC) at CERN/Geneva. It will replace the current end-cap calorimeters for data acquisition in 2027. The design of the new calorimeter is driven by the increased particle density following the High Luminosity Upgrade of the LHC. This enforces a complex geometry of the active sensor and a massive increase from about 7k currently to more than 3 million individual channels for each of the two future end-cap calorimeters. Traditionally, particle physics has used detailed simulations of detectors and physics. These simulations are of high precision and can describe minute details. The price for this precision is a large computational power requirement. Since the predicted computational resources will not be sufficient for the traditional stepwise simulations, we will explore deep learning techniques combining fast inference with generative models, e.g., physics-informed GANs-VAEs hybrids for simulations. In particular, Graph Neural Networks are a promising candidate to describe thecomplex geometry of the new detector. This project is part of the Helmholtz AI funded project DeGeSim.
000905718 536__ $$0G:(DE-HGF)POF4-5112$$a5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
000905718 536__ $$0G:(DE-HGF)2020_ZT-I-PF-5-3$$aZT-I-PF-5-3 - Deep Generative models for fast and precise physics Simulation (DeGeSim) (2020_ZT-I-PF-5-3)$$c2020_ZT-I-PF-5-3$$x1
000905718 7001_ $$0P:(DE-HGF)0$$aBhattacharya, Soham$$b1
000905718 7001_ $$0P:(DE-HGF)0$$aBorras, Kerstin$$b2
000905718 7001_ $$0P:(DE-HGF)0$$aFedorko, Wojtek$$b3
000905718 7001_ $$0P:(DE-Juel1)158080$$aJitsev, Jenia$$b4$$eCorresponding author$$ufzj
000905718 7001_ $$0P:(DE-HGF)0$$aKatzy, Judith$$b5$$eCorresponding author
000905718 7001_ $$0P:(DE-HGF)0$$aKrücker, Dirk$$b6$$eCorresponding author
000905718 8564_ $$uhttps://www.helmholtz.ai/fileadmin/HAICU/PDF/HelmholtzAIcon21_AbstractBook_DinA4_FV.pdf
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000905718 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)187009$$aForschungszentrum Jülich$$b0$$kFZJ
000905718 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)158080$$aForschungszentrum Jülich$$b4$$kFZJ
000905718 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5112$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
000905718 9141_ $$y2021
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