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
Dirk Kruecker
(CMS (CMS Fachgruppe Searches))
Engin Eren
(FLC (FTX Fachgruppe SFT))
Frank Gaede
(FTX (FTX Fachgruppe SFT))
Gregor Kasieczka
(UNI/EXP (Uni Hamburg, Institut fur Experimentalphysik))
Moritz Scham
(CMS (CMS Fachgruppe Searches))
Moritz Wolf
(UNI/EXP (Uni Hamburg, Institut fur Experimentalphysik))
Peter McKeown
(FTX (FTX Fachgruppe SFT))
Samuel Bein
(UNI/EXP (Uni Hamburg, Institut fur Experimentalphysik))
Soham Bhattacharya
(CMS (CMS Fachgruppe Searches))
William Korcari
(UNI/EXP (Uni Hamburg, Institut fur Experimentalphysik))
