Poster

PUBDB-2022-04336

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Generative modeling with Graph Neural Networks for the CMS HGCal

Scham, M. (Corresponding author)DESY* ; Bhattacharya, S.DESY* ; Borras, K.DESY* ; Bein, S. ; Eren, E. ; Gaede, F. ; Kasieczka, G. ; Korcari, W. ; Krücker, D.DESY* ; McKeown, P. ; CMS Collaboration (Collaboration author)DESY*

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.

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Contributing Institute(s):

1. LHC/CMS Experiment (CMS)

Research Program(s):

1. 611 - Fundamental Particles and Forces (POF4-611) (POF4-611)

Experiment(s):

1. LHC: CMS

Appears in the scientific report 2022

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