Speaker
Moritz Scham
(CMS (CMS Fachgruppe Searches))
Description
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.
Primary authors
Soham Bhattacharya
(CMS (CMS Fachgruppe Searches))
Samuel Bein
(UNI/EXP (Uni Hamburg, Institut fur Experimentalphysik))
Kerstin Borras
(DESY / RWTH Aachen University)
Engin Eren
(FLC (FTX Fachgruppe SFT))
Frank Gaede
(FTX (FTX Fachgruppe SFT))
Gregor Kasieczka
(UNI/EXP (Uni Hamburg, Institut fur Experimentalphysik))
William Korcari
(UNI/EXP (Uni Hamburg, Institut fur Experimentalphysik))
Dirk Krücker
(DESY)
Peter McKeown
(FTX (FTX Fachgruppe SFT))
Moritz Scham
(CMS (CMS Fachgruppe Searches))
Moritz Wolf
(UNI/EXP (Uni Hamburg, Institut fur Experimentalphysik))
