TY  - CONF
AU  - Scham, Moritz
AU  - Bhattacharya, Soham
AU  - Borras, Kerstin
AU  - Bein, Sam
AU  - Eren, Engin
AU  - Gaede, Frank
AU  - Kasieczka, Gregor
AU  - Korcari, William
AU  - Krücker, Dirk
AU  - McKeown, Peter
TI  - Generative modeling with Graph Neural Networks for the CMS HGCal
M1  - PUBDB-2022-04336
PY  - 2022
AB  - In high energy physics, detailed and time-consuming simulations are used for particle interactions with detectors. For the upcoming High-Luminosity phase of the Large Hadron Collider (HL-LHC), the computational costs of conventional simulation tools exceeds the projected computational resources. Generative machine learning is expected to provide a fast and accurate alternative. The CMS experiment at the LHC will use a new High Granularity Calorimeter (HGCal) to cope with the high particle density. The new HGCal is an imaging calorimeter with a complex geometry and more than 3 million cells. We report on the development of a GraphGAN to simulate particle showers under these challenging conditions.
T2  - Center for Data and Computing in Natural Sciences (CDCS) Symposium
CY  - 26 Apr 2022 - 28 Apr 2022, Hamburg (Germany)
Y2  - 26 Apr 2022 - 28 Apr 2022
M2  - Hamburg, Germany
LB  - PUB:(DE-HGF)24
UR  - https://bib-pubdb1.desy.de/record/481363
ER  -