Speaker
Description
We present advances in hadronic object tagging for the Phase 2 Upgrade of the CMS Level 1 Trigger. With advances in Fast Machine Learning; more powerful FPGA processors; and the introduction of track and particle flow reconstruction at the Level 1 Trigger, jet tagging will become feasible for the first time in this system. We show the implementation of tiny jet taggers using a DeepSets architecture with only O(1000) parameters. Our models are capable of processing 1 billion jets per second and with a latency of around 200 ns. Furthermore, we will present the physics performance enhancements that jet tagging can bring to the Level 1 Trigger. For the first time, boosted resonances are reconstructed as large-radius jets with dedicated algorithms, enabling trigger selection based on jet mass and substructure. In addition, a multi-class jet tagger on small-radius jets allows us to select physics targets such as di-Higgs events with looser kinematic thresholds. These results represent a significant step towards a more “physics-aware” trigger system capable of targeting and preserving challenging signals despite the high-luminosity environment.