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@INPROCEEDINGS{Scham:481363,
author = {Scham, Moritz and Bhattacharya, Soham and Borras, Kerstin
and Bein, Sam and Eren, Engin and Gaede, Frank and
Kasieczka, Gregor and Korcari, William and Krücker, Dirk
and McKeown, Peter},
collaboration = {{CMS Collaboration}},
title = {{G}enerative modeling with {G}raph {N}eural {N}etworks for
the {CMS} {HGC}al},
reportid = {PUBDB-2022-04336},
year = {2022},
abstract = {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.},
month = {Apr},
date = {2022-04-26},
organization = {Center for Data and Computing in
Natural Sciences (CDCS) Symposium,
Hamburg (Germany), 26 Apr 2022 - 28 Apr
2022},
cin = {CMS},
cid = {I:(DE-H253)CMS-20120731},
pnm = {611 - Fundamental Particles and Forces (POF4-611)},
pid = {G:(DE-HGF)POF4-611},
experiment = {EXP:(DE-H253)LHC-Exp-CMS-20150101},
typ = {PUB:(DE-HGF)24},
url = {https://bib-pubdb1.desy.de/record/481363},
}