Speakers
Description
Charged particle track reconstruction is one the heaviest computational tasks in the event reconstruction chain at Large Hadron Collider (LHC) experiments. Furthermore, projections for the High Luminosity LHC (HL-LHC) show that the required computing resources for single-threaded CPU algorithms will exceed those that are expected to be available. It follows that experiments at the HL-LHC will need to employ novel and evolved track reconstruction algorithms, within heterogeneous computing systems that include many-core CPUs as well as GPUs, in the attempt to maximize the computational performance while retaining the best possible reconstruction efficiency. In the context of the CMS High Level Trigger (HLT) at the HL-LHC, the mkFit algorithm, already in use for the CMS track reconstruction during the LHC Run 3, will exploit its parallelized and vectorized nature on CPUs to perform pattern recognition using seed tracks produced with algorithms that are designed to be fully parallelizable and hardware agnostic, thus suitable for heterogeneous systems: the Patatrack and the Line Segment Tracking (LST) algorithms. Patatrack is an established algorithm, already used for the CMS pixel track reconstruction at HLT during the Run 3 of the LHC, while LST is a novel algorithm, recently integrated in the CMS software, targeting the reconstruction of tracks in the outer tracker of the HL-LHC CMS detector. The state-of-the-art performance for the CMS HLT track reconstruction at the HL-LHC is presented, obtained using the combination of the mkFit, Patatrack and LST algorithms, that in turn use ML techniques such as deep neural networks and multi-objective particle swarm optimization to suppress duplicate and misconstructed tracks. Prospects of further improvements are also presented, with a focus on the usage of ML techniques for track reconstruction at CMS.
References
https://cms.cern.ch/iCMS/jsp/db_notes/showNoteDetails.jsp?noteID=CMS%20DP-2022/014
https://cms.cern.ch/iCMS/jsp/db_notes/showNoteDetails.jsp?noteID=CMS%20DP-2022/018
https://cms.cern.ch/iCMS/jsp/db_notes/showNoteDetails.jsp?noteID=CMS%20DP-2023/019
https://cms.cern.ch/iCMS/jsp/db_notes/showNoteDetails.jsp?noteID=CMS%20DP-2023/075
https://cms.cern.ch/iCMS/jsp/db_notes/showNoteDetails.jsp?noteID=CMS%20DP-2024/014
https://cms.cern.ch/iCMS/jsp/db_notes/showNoteDetails.jsp?noteID=CMS%20DP-2024/083
https://cms.cern.ch/iCMS/jsp/db_notes/showNoteDetails.jsp?noteID=CMS%20DP-2024/084
| Experiment context, if any | CMS experiment |
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