Machine-learning based particle-flow algorithm in CMS

The CMS particle-flow (PF) algorithm [1] uses rule-based methods, such as proximity-based linking--associating tracks and calorimeter clusters--to reconstruct a global, particle-level view of each event. In contrast, machine-learned particle-flow (MLPF) uses transformer models trained on simulation to exploit low-level features of particle interactions with the detector that may not be immediately obvious from a first principles approach based on feature engineering. In these proceedings, we present an MLPF implementation integrated within the CMS software framework ( CMSSW), trained on Monte Carlo (MC) simulation samples with pileup (PU) and validated both in simulation and on proton-proton collisions data collected during Run 3 (2022 - 2026) by the CMS experiment [2, 3].