Experimental Research at the Energy Frontier in High Energy Physics with the CMS Experiment

G. Mitselmakher, University of Florida, (Principal Investigator)

D. Acosta, University of Florida, (Co-Investigator)

P. Avery, University of Florida, (Co-Investigator)

J. Konigsberg, University of Florida, (Co-Investigator)

A. Korytov, University of Florida, (Co-Investigator)

The research program of the University of Florida’s experimental hadron collider group at the CMS experiment at the Large Hadron Collider (LHC) at CERN builds on the group’s strengths, expands previous areas of research into new directions, and opens up new areas of study. The program consists of tasks on detector operation, upgrades, and physics analyses of the LHC data acquired by CMS. Members of our group have various official coordination roles across each of these areas.

The Florida CMS group bears responsibility for the operations and maintenance of the CMS detector systems that the group has built: The endcap muon system Cathode Strip Chambers (CSC), their high-voltage system, the Level-1 endcap muon trigger, data quality monitoring, and local data reconstruction for the CSC. The University of Florida Tier-2 computing center supports computing, software, and datasets for CMS physicists. 

The group’s upgrade activities include the Endcap Muon Trigger, the Global Muon Trigger, and R&D on CSC longevity towards their operation during the High-Luminosity LHC era. Specifically, custom made electronic systems and innovative firmware solutions are planned be employed in the trigger systems, including real-time Machine Learning algorithms that expand the capabilities of the CMS physics program.

The group’s physics program includes studies of the Higgs boson decays in three modes: 4-lepton, bb and µµ final states; searches for di-Higgs production, and searches of SUSY with leptons in the final state. The group also plans to search for lepton flavor violating decays τ→3µ; low-mass dilepton resonances in decays of Higgs, and Z and W-bosons.  During the next project period, the group plans to apply innovative analysis techniques that include Machine Learning, which are extensively used to enhance the sensitivity and impact of the results.