Conference Presentation (After Call)

PUBDB-2023-07982

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

Buss, T. L. H. (Corresponding author)DESY* ; Diefenbacher, S. D.Extern* ; Gaede, F.DESY* ; Kasieczka, G.Extern* ; Krause, C. ; 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. It has been previously demonstrated, that such flows can generate showers in calorimeters with high accuracy. However, the main drawback of normalizing flows with fully connected sub-networks is that they scale poorly with input dimensions. We overcome this issue by using a U-Net based flow architecture and show how it can be applied to accurately simulate showers in highly granular calorimeters.

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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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