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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 $\mu$s, 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. In this talk we will describe the implementation, its jet reconstruction performance, computational metrics, and the developments towards jet tagging. 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 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. Summers, Sioni; Bestintzanos, Ioannis; Petrucciani, Giovanni" /> <meta name="keywords" content="Trigger" /> <script type="text/javascript" src="https://cds.cern.ch/js/jquery.min.js"></script>  <link rel="stylesheet" href="https://cds.cern.ch/img/webnews.css" type="text/css" />  <script type="text/javascript" src="https://cds.cern.ch/js/webnews.js?v=20131009"></script> <meta property="fb:app_id" content="137353533001720"/> <script type="text/x-mathjax-config"> MathJax.Hub.Config({ tex2jax: {inlineMath: [['$','$']], processEscapes: true}, showProcessingMessages: false, messageStyle: "none" }); </script> <script src="/MathJax/MathJax.js?config=TeX-AMS_CHTML" type="text/javascript"> </script>  <meta content="EDP Sciences : Reconstructing jets in the Phase-2 upgrade of the CMS Level-1 Trigger with a seeded cone algorithm" name="citation_title" /> <meta content="Summers, Sioni" name="citation_author" /> <meta content="Bestintzanos, Ioannis" name="citation_author" /> <meta content="Petrucciani, Giovanni" name="citation_author" /> <meta content="10.1051/epjconf/202429502024" name="citation_doi" /> <meta content="EPJ Web Conf." name="citation_journal_title" /> <meta content="295" name="citation_volume" /> <meta content="02024" name="citation_firstpage" /> <meta content="2023/08/16" name="citation_publication_date" /> <meta name="citation_online_date" content="2023/09/25"> <meta content="arXiv:2310.08062" name="citation_technical_report_number" /> <meta content="CMS-CR-2023-118" name="citation_technical_report_number" /> <meta content="10.1051/epjconf/202429502024" name="citation_doi" /> <meta name="citation_pdf_url" content="https://cds.cern.ch/record/2872269/files/CR2023_118.pdf" /> <meta name="citation_pdf_url" content="https://cds.cern.ch/record/2872269/files/2310.08062.pdf" /> <meta name="citation_pdf_url" content="https://cds.cern.ch/record/2872269/files/document.pdf" />  <meta content="Reconstructing jets in the Phase-2 upgrade of the CMS Level-1 Trigger with a seeded cone algorithm" property="og:title" /> <meta content="EDP Sciences" property="og:title" /> <meta content="website" property="og:type" /> <meta content="website" property="og:type" /> <meta content="website" property="og:type" /> <meta content="https://cds.cern.ch/record/2872269" property="og:url" /> <meta name="twitter:image" content="https://cds.cern.ch/record/2872269/files/qcd_effi_threshs.png" /> <meta property="og:image" content="https://cds.cern.ch/record/2872269/files/qcd_effi_threshs.png" /> <meta property="og:image:secure_url" content="https://cds.cern.ch/record/2872269/files/qcd_effi_threshs.png" /> <meta property="og:image" content="https://cds.cern.ch/record/2872269/files/qcd_effi_70kHz.png" /> <meta property="og:image:secure_url" content="https://cds.cern.ch/record/2872269/files/qcd_effi_70kHz.png" /> <meta property="og:image" content="https://cds.cern.ch/record/2872269/files/ttbar_scakmatch.png" /> <meta property="og:image:secure_url" content="https://cds.cern.ch/record/2872269/files/ttbar_scakmatch.png" /> <meta property="og:image" content="https://cds.cern.ch/record/2872269/files/qcd_scakmatch.png" /> <meta property="og:image:secure_url" content="https://cds.cern.ch/record/2872269/files/qcd_scakmatch.png" /> <meta property="og:image" content="https://cds.cern.ch/record/2872269/files/scj_timeline.png" /> <meta property="og:image:secure_url" content="https://cds.cern.ch/record/2872269/files/scj_timeline.png" /> <meta property="og:image" content="https://cds.cern.ch/record/2872269/files/minbias_rate_sc4ak4.png" /> <meta property="og:image:secure_url" content="https://cds.cern.ch/record/2872269/files/minbias_rate_sc4ak4.png" /> <meta property="og:image" content="https://cds.cern.ch/record/2872269/files/ttbar_effi_70kHz.png" /> <meta property="og:image:secure_url" content="https://cds.cern.ch/record/2872269/files/ttbar_effi_70kHz.png" /> <meta property="og:image" content="https://cds.cern.ch/record/2872269/files/ttbar_effi_threshs.png" /> <meta property="og:image:secure_url" content="https://cds.cern.ch/record/2872269/files/ttbar_effi_threshs.png" /> <meta property="og:image" content="https://cds.cern.ch/record/2872269/files/deregionizer_truncation.png" /> <meta property="og:image:secure_url" content="https://cds.cern.ch/record/2872269/files/deregionizer_truncation.png" /> <meta property="og:image" content="https://cds.cern.ch/record/2872269/files/scj_floorplan.png" /> <meta property="og:image:secure_url" content="https://cds.cern.ch/record/2872269/files/scj_floorplan.png" /> <meta content="CERN Document Server" property="og:site_name" /> <meta content="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 $\mu$s, 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. 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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. 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