Nov 4 – 8, 2024
LPNHE, Paris, France
Europe/Paris timezone

ML assisted Event Reconstruction in the CMS Phase-2 High Granularity Calorimeter Endcap

Nov 8, 2024, 9:40 AM
20m
Amphi Charpak

Amphi Charpak

Speaker

Theo Cuisset (LLR / École Polytechnique (FR))

Description

The high-luminosity era of the LHC will pose unprecedented challenges to the detectors. To meet these challenges, the CMS detector will undergo several upgrades, including the replacement the current endcap calorimeters with a novel High-Granularity Calorimeter (HGCAL). To make optimal use of this innovative detector, novel algorithms have to be invented. A dedicated reconstruction framework, The Iterative Clustering (TICL), is being developed within the CMS Software (CMSSW). This new framework is designed to fully exploit the high spatial resolution and precise timing information provided by HGCAL. Several key ingredients of the object reconstruction chain already rely on Machine Learning techniques and their usage is expected to further develop in the future. In the presentation, the reconstruction existing strategies will be presented stressing the role played by ML techniques to exploit the information provided by the detector. The areas where ML techniques are expected to play a role in the future developments will be also discussed

Co-authors

Hongbo Liao (Chinese Academy of Sciences (CN)) Theo Cuisset (LLR / École Polytechnique (FR))

Presentation materials