Conference Presentation (After Call)

PUBDB-2023-07983

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Generating Accurate Showers in Highly Granular Calorimeters Using Normalizing Flows

Buss, T. L. H. (Corresponding author)Extern*DESY* ; Diefenbacher, S. D.Extern* ; Eren, E.DESY* ; Gaede, F.DESY* ; Kasieczka, G.Extern* ; Krause, C. ; Shekhzadeh, I.Extern* ; Shih, D.

2023

Abstract: The 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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Contributing Institute(s):

1. Uni Hamburg / Experimentalphysik (UNI/EXP)
2. Technol. zukünft. Teilchenph. Experim. (FTX)

Research Program(s):

1. 623 - Data Management and Analysis (POF4-623) (POF4-623)
2. DFG project 390833306 - EXC 2121: Quantum Universe (390833306) (390833306)
3. 05D23GU4 - Verbundprojekt 05D2022 - KISS: Künstliche Intelligenz zur schnellen Simulation von wissenschaftlichen Daten. Teilprojekt 1. (BMBF-05D23GU4) (BMBF-05D23GU4)

Experiment(s):

1. No specific instrument

Appears in the scientific report 2023

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