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May 8 – 12, 2023
Norfolk Waterside Marriott
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Generating Accurate Showers in Highly Granular Calorimeters Using Normalizing Flows

May 11, 2023, 2:15 PM
Marriott Ballroom I (Norfolk Waterside Marriott)

Marriott Ballroom I

Norfolk Waterside Marriott

235 East Main Street Norfolk, VA 23510
Oral Track 9 - Artificial Intelligence and Machine Learning Track 3+9 Crossover


Mr Buss, Thorsten (Universität Hamburg)


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.

Consider for long presentation No

Primary authors

Mr Buss, Thorsten (Universität Hamburg) Mr Diefenbacher, Sascha (Universität Hamburg) Dr Eren, Engin (DESY) Dr Gaede, Frank (DESY) Prof. Kasieczka, Gregor (Universität Hamburg) Dr Krause, Claudius (Universität Heidelberg) Mr Shekhzadeh, Imahn (Université de Genéve) Prof. Shih, David (Rutgers University)

Presentation materials