Detecting Muon Momentum in the CMS Experiment at CERN using Deep Learning

Detecting Muon Momentum in the CMS Experiment at CERN using Deep Learning

How we built and trained Neural Networks in Tensorflow for muon momentum prediction for the CMS experiment at CERN.

The detection of muons is an important task in the CMS experiment at CERN (European Organization for Nuclear Research). Muons are usually expected to be produced by many physical experiments studied in depth at the CMS physics program at CERN. For example, one way to study the famous Higgs Boson particle is through a decay channel where the Higgs Boson decays into four muons. Since muons can penetrate several meters of iron with no significant energy loss, they are hardly stopped by any layer of the CMS system. Therefore, the muon detectors are the outermost ones, far from the interaction point. The current muon transverse momentum detector, the Barrel Muon Track Finder, uses Lookup Tables to estimate the transverse momentum of the muons. The latter is measured using the pseudo-rapidity eta, a spatial quantity related to the angle at which the charged particles emerge from these collisions, and the azimuth angle phi, the angle that the collision paths make with the z-axis.

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