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The Phase-2 Upgrade of the CMS Level-1 Trigger (L1T) will reconstructparticles using the Particle Flow algorithm, connecting information from thetracker, muon, and calorimeter detectors, and enabling fine-grainedreconstruction of high level physics objects like jets. We have developed a jetreconstruction algorithm using a cone centred on an energetic seed from theseParticle Flow candidates. The implementation is designed to find up to 16 jetsin each Xilinx Ultrascale+ FPGA, with a latency of less than 1 {\mu}s, andevent throughput of 6.7 MHz to fit within the L1T system constraints. Pipelinedprocessing enables reconstruction of jet collections with different cone sizesfor little additional resource cost. The design of the algorithm also providesa platform for additional computation using the jet constituents, such as jettagging using neural networks. We will describe the implementation, its jetreconstruction performance, computational metrics, and the developments towardsjet tagging.

Reconstructing jets in the Phase-2 upgrade of the CMS Level-1 Trigger with a seeded cone algorithm