Speaker
Description
At the Phase-2 Upgrade of the CMS Level-1 Trigger (L1T), particles will be reconstructed by linking charged particle tracks with clusters in the calorimeters and muon tracks from the muon stations. The 200 pileup interactions will be mitigated using primary vertex reconstruction for charged particles and a weighting for neutral particles based on the distribution of energy in a small area. Jets will be reconstructed from these pileup-subtracted particles using a fast cone algorithm. For the first time at the CMS L1T, the particle constituents of jets will be available, opening numerous opportunities to effectively leverage machine learning for multiple different purposes. This talk presents two machine learning models for tagging jets of different radii. Both follow a similar strategy of processing the jet constituents separately in a Deep Sets architecture before aggregating. For narrow jets, the jet flavor as well as the momentum are essential for designing effective trigger seeds. A Deep Sets-based architecture proves promising for both jet-flavor identification and the regression of a momentum correction factor for the jet. The model distinguishes between light-flavor jets ($uds$), gluon, $b$, and $c$ jets, as well as $\tau^+$, $\tau^-$, electron, and muon jets, going far beyond previous jet-tagging developments. For momentum regression, the model predicts individual corrections for each constituent, which are then combined to derive a correction factor for the entire jet. This approach improves upon the existing jet energy corrections, particularly in the reconstruction of invariant masses of particles. For the first time, boosted resonances will be reconstructed with dedicated large radius jet reconstruction. A neural network has been trained to identify substructure within large radius jets, enhancing triggering on boosted resonances.