Barrel muon track reconstruction with deep learning for Level-1 trigger data scouting in the CMS experiment

In anticipation of the High Luminosity LHC, the CMS Level-1 trigger data scouting system stands to substantially extend the physics reach of the experiment. Utilizing the existing Run-3 data scouting demonstrator as a testbed, this study introduces new methodologies to refine muon track reconstruction processes in the barrel region at the Level-1 trigger. The document reports on three pivotal advancements. Firstly, it offers the initial investigation and validation of trigger muon track segments, termed super-primitives, generated within the trigger chain. Secondly, the study undertakes the validation of the software emulator corresponding to the current muon reconstruction algorithm for the barrel region. Lastly, the study employs machine learning algorithms, specifically engineered for FPGA deployment, to leverage stub-only information for fast, online reconstruction of muon physical parameters. These algorithms yield a slight improvement over standard reconstruction algorithms.