TY  - CONF
AU  - Buss, Thorsten Lars Henrik
AU  - Diefenbacher, Sascha Daniel
AU  - Gaede, Frank
AU  - Kasieczka, Gregor
AU  - Krause, Claudius
AU  - Shih, David
AU  - Eren, Engin
AU  - Shekhzadeh, Imahn
TI  - Generating Accurate Showers in Highly Granular Calorimeters Using Normalizing Flows
PB  - Universitaet Hamburg
M1  - PUBDB-2023-07980
PY  - 2023
AB  - 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.
T2  - 26th International Conference on Computing in High Energy & Nuclear Physics
CY  - 8 May 2023 - 12 May 2023, Norfolk (USA)
Y2  - 8 May 2023 - 12 May 2023
M2  - Norfolk, USA
LB  - PUB:(DE-HGF)6
UR  - https://bib-pubdb1.desy.de/record/600423
ER  -