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000600426 041__ $$aEnglish
000600426 1001_ $$0P:(DE-H253)PIP1103765$$aBuss, Thorsten Lars Henrik$$b0$$eCorresponding author
000600426 1112_ $$aDPG Frühjahrstagung$$cDresden$$d2023-03-20 - 2023-03-24$$wGermany
000600426 245__ $$aGenerating Accurate Showers in Highly Granular Calorimeters Using Normalizing Flows
000600426 260__ $$c2023
000600426 3367_ $$033$$2EndNote$$aConference Paper
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000600426 520__ $$aThe full simulation of particle colliders incurs a significant computational cost. Among the most resource-intensive steps are detector simulations. It is expected that future developments, such as higher collider luminosities and highly granular calorimeters, will increase the computational resource requirement for simulation beyond availability. One possible solution is generative neural networks that can accelerate simulations. Normalizing flows are a promising approach in this pursuit. It has been previously demonstrated, that such flows can generate showers in low-complexity calorimeters with high accuracy. We show how normalizing flows can be improved and adapted for precise shower simulation in significantly more complex calorimeter geometries.
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000600426 7001_ $$0P:(DE-H253)PIP1090777$$aDiefenbacher, Sascha Daniel$$b1
000600426 7001_ $$0P:(DE-H253)PIP1020256$$aEren, Engin$$b2$$udesy
000600426 7001_ $$0P:(DE-H253)PIP1002530$$aGaede, Frank$$b3$$udesy
000600426 7001_ $$0P:(DE-H253)PIP1081743$$aKasieczka, Gregor$$b4
000600426 7001_ $$0P:(DE-HGF)0$$aKrause, Claudius$$b5
000600426 7001_ $$0P:(DE-H253)PIP1093142$$aShekhzadeh, Imahn$$b6
000600426 7001_ $$0P:(DE-HGF)0$$aShih, David$$b7
000600426 8564_ $$uhttps://www.dpg-verhandlungen.de/year/2023/conference/smuk/part/akpik/session/8/contribution/2
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000600426 9141_ $$y2023
000600426 9201_ $$0I:(DE-H253)UNI_EXP-20120731$$kUNI/EXP$$lUni Hamburg / Experimentalphysik$$x0
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