%0 Conference Paper
%A Scham, Moritz
%A Bhattacharya, Soham
%A Borras, Kerstin
%A Bein, Sam
%A Eren, Engin
%A Gaede, Frank
%A Kasieczka, Gregor
%A Korcari, William
%A Krücker, Dirk
%A McKeown, Peter
%T Generative modeling with Graph Neural Networks for the CMS HGCal
%M PUBDB-2022-04336
%D 2022
%X 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.
%B Center for Data and Computing in Natural Sciences (CDCS) Symposium
%C 26 Apr 2022 - 28 Apr 2022, Hamburg (Germany)
Y2 26 Apr 2022 - 28 Apr 2022
M2 Hamburg, Germany
%F PUB:(DE-HGF)24
%9 Poster
%U https://bib-pubdb1.desy.de/record/481363