ClusterJet: A Manual Cut-Based Boosted Jet Algorithm for the CMS Level-1 Trigger

Abstract

The Standard Model of particle physics provides a solid theoretical framework for fundamental particles and their interactions, delivering predictions consistent with many experimental results. However, it fails to explain certain observed phenomena, motivating searches for Beyond the Standard Model (BSM) physics at the Large Hadron Collider (LHC) at CERN. The Compact Muon Solenoid (CMS) experiment studies the 13.6 TeV proton-proton collisions delivered by the LHC, requiring an efficient trigger system to select interesting events out of a collision rate of 40 million per second. This thesis investigates the development, performance, and implementation of a manual cut-based clustering algorithm, clusterjet, designed to improve sensitivity to highly boosted jet topologies at the Level-1 (L1) trigger. Monte Carlo simulations of highly boosted Higgs bosons decaying into bottom quark-antiquark pairs, along with Zero Bias data from Run 3, are used to assess the algorithm’s physics performance. The proposed clusterjet trigger shows improved efficiency and resolution at high transverse momentum compared to existing L1 algorithms, accepting more events in the target high-pT region. However, latency studies using VIVADO HLS suggest that clusterjet does not meet the strict timing requirements for L1 implementation. Future work may focus on overcoming FPGA timing constraints and exploring alternative clustering approaches.