An enhanced track reconstruction for the CMS experiment at the HL-LHC
Abstract
This contribution presents the new phase-2 CMS baseline tracking strategy currently proposed for online event reconstruction, which can be similarly useful in offline reconstruction under the extreme pileup conditions of the High-Luminosity LHC (HL-LHC). By unifying diverse algorithmic paradigms and integrating machine learning (ML) techniques into a coherent sequence, CMS can maintain high-efficiency and high-resolution tracking while improving the fake track rate and the computational performance, a crucial aspect especially for the HL-LHC. The approach combines GPU-optimized algorithms (Patatrack, LST) with vectorized CPU methods (mkFit), exploiting modern hardware to maximize throughput. This heterogeneous strategy reduces the resource requirements and enhances the physics reach by incorporating a global displaced track reconstruction and extending sensitivity to rare, interesting signatures, such as long-lived particles.
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BibTeX
@misc{masciovecchio2026enhanced,
title = {{An enhanced track reconstruction for the CMS experiment at the HL-LHC}},
author = {Mario Masciovecchio},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.26383},
eprint = {2609.26383},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.26383},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - An enhanced track reconstruction for the CMS experiment at the HL-LHC
AU - Mario Masciovecchio
PY - 2026
DA - 2026/09/22
PB - arXiv
DO - 10.48550/arxiv.2609.26383
UR - https://arxiv.org/abs/2609.26383
AB - This contribution presents the new phase-2 CMS baseline tracking strategy currently proposed for online event reconstruction, which can be similarly useful in offline reconstruction under the extreme pileup conditions of the High-Luminosity LHC (HL-LHC). By unifying diverse algorithmic paradigms and integrating machine learning (ML) techniques into a coherent sequence, CMS can maintain high-efficiency and high-resolution tracking while improving the fake track rate and the computational performance, a crucial aspect especially for the HL-LHC. The approach combines GPU-optimized algorithms (Patatrack, LST) with vectorized CPU methods (mkFit), exploiting modern hardware to maximize throughput. This heterogeneous strategy reduces the resource requirements and enhances the physics reach by incorporating a global displaced track reconstruction and extending sensitivity to rare, interesting signatures, such as long-lived particles.
ER -