SciPost Submission Page
Machine Learning for Event Reconstruction in the CMS Phase-2 High Granularity Calorimeter Endcap
by Théo Cuisset
Submission summary
| Authors (as registered SciPost users): | Théo Cuisset |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/2510.01851v2 (pdf) |
| Date submitted: | Dec. 9, 2025, 12:12 p.m. |
| Submitted by: | Théo Cuisset |
| Submitted to: | SciPost Physics Proceedings |
| Proceedings issue: | The 2nd European AI for Fundamental Physics Conference (EuCAIFCon2025) |
| Ontological classification | |
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| Academic field: | Physics |
| Specialties: |
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| Approaches: | Experimental, Computational |
The author(s) disclose that the following generative AI tools have been used in the preparation of this submission:
Used GPT-5 for language advice
Abstract
The high-luminosity era of the LHC will offer greatly increased number of events for more precise Standard Model measurements and Beyond Standard Model searches, but will also pose unprecedented challenges to the detectors. To meet these challenges, the CMS detector will undergo several upgrades, including the replacement of the current endcap calorimeters with a novel High-Granularity Calorimeter (HGCAL). To make optimal use of this innovative detector, new and original algorithms are being devised. 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 (ML) techniques and their usage is expected to further develop in the future. The existing reconstruction 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.
Author comments upon resubmission
List of changes
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added mention of the occupancy in the detector (along with citation of HGCAL TDR) in Introduction
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removed Figure 4 (schematic description of a bremsstrahlung)
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added a quantified improvement for energy resolution for hadron energy regression (Section 5)