Issue |
EPJ Web of Conf.
Volume 295, 2024
26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023)
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|
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Article Number | 02006 | |
Number of page(s) | 8 | |
Section | Online Computing | |
DOI | https://doi.org/10.1051/epjconf/202429502006 | |
Published online | 06 May 2024 |
https://doi.org/10.1051/epjconf/202429502006
Cluster reconstruction in the HGCAL at the Level 1 trigger
Laboratoire Leprince-Ringuet, École Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
* e-mail: bruno.alves@cern.ch
Published online: 6 May 2024
The CMS collaboration has chosen a novel High Granularity Calorimeter for the endcap regions as part of its planned upgrade for the High Luminosity LHC. The calorimeter will have fine segmentation in both the transverse and longitudinal directions, and its data will be part of the Level 1 trigger of the CMS experiment. The trigger has tight constraints on latency and rate, and will need to be implemented in hardware. The high granularity results in around six million readout channels in total, reduced to one million that are used at 40 MHz as part of the Level 1 trigger, presenting a significant challenge in terms of data manipulation and processing; the trigger data volumes will be an order of magnitude above those currently handled at CMS. In addition, the high luminosity will result in an average of 140 (or more) interactions per bunch crossing. This leads to a huge rate by background processes which must be efficiently rejected by the trigger algorithms. Furthermore, reconstruction of the particle clusters to be used for particle flow in events with high hit rates is also a complex computational problem for the trigger. The status of the cluster reconstruction algorithms developed to tackle these major challenges, as well as the associated trigger architecture, is presented. Methods developed to mitigate the known issue of cluster splitting are described, incuding an iterative algorithm which has no impact on firmware resources.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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