Machine-Learning Long-Lived Particle Jet Tagger for the CMS Level-1 Trigger

To account for discrepancies between cosmological observations and standard model predictions, Beyond the Standard Model (BSM) theories predict the existence of Long-Lived particles (LLPs) that may be detected in the Compact Muon Solenoid (CMS) detector through jets with displaced vertices. Such displaced jets represent an exciting opportunity for new physics to be found in particle colliders. At the Large Hadron Collider (LHC), The Level-1 trigger at the CMS detector selects events of interest for further analysis. However, this trigger may be discarding the events with potential new physics because of biases for SM signals. To address this matter, we present the development of a machine learning LLP jet tagger to be incorporated in the CMS Level-1 trigger technology during the Phase-2 upgrade.