| Issue |
EPJ Web of Conf.
Volume 295, 2024
26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023)
|
|
|---|---|---|
| Article Number | 02024 | |
| Number of page(s) | 8 | |
| Section | Online Computing | |
| DOI | https://doi.org/10.1051/epjconf/202429502024 | |
| Published online | 06 May 2024 | |
https://doi.org/10.1051/epjconf/202429502024
Reconstructing jets in the Phase-2 upgrade of the CMS Level-1 Trigger with a seeded cone algorithm
1 CERN Geneva, Switzerland
2 University of Ioannina Ioannina, Greece
* e-mail: sioni@cern.ch
Published online: 6 May 2024
The Phase-2 Upgrade of the CMS Level-1 Trigger (L1T) will reconstruct particles using the Particle Flow algorithm, connecting information from the tracker, muon, and calorimeter detectors, and enabling fine-grained reconstruction of high level physics objects like jets. We have developed a jet reconstruction algorithm using a cone centred on an energetic seed from these Particle Flow candidates. The implementation is designed to find up to 16 jets in each Xilinx Ultrascale+ FPGA, with a latency of less than 1 µs, and event throughput of 6.7 MHz to fit within the L1T system constraints. Pipelined processing enables reconstruction of jet collections with different cone sizes for little additional resource cost. The design of the algorithm also provides a platform for additional computation using the jet constituents, such as jet tagging using neural networks. We will describe the implementation, its jet reconstruction performance, computational metrics, and the developments towards jet tagging.
© 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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