|Date||November 14, 2018||Time||4:00 - 5:00 pm|
|Location||Baylor Sciences Building, Room E.125|
Joe Pastika, Ph.D.
HCAL Endcap Upgrade & Top Squark Search with Top Tagging
With the coming high-luminosity upgrade of the Large Hadron Collider (LHC) slated to complete in 2027, the CMS detector must undergo a series of upgrades to cope with the increased rate of particle interactions. The first set of these upgrades, called the Phase 1 upgrades, will enhance the performance of the hadronic calorimeter, pixel tracker, and muon systems. In addition, the techniques used to analyze the collision data must also be optimized to maintain a high level of performance and make the best use of the huge volume of data to come. This presentation will focus on the Phase 1 upgrade of the hadron calorimeter and a search for top squarks using machine learning techniques. The upgrade of the hadron calorimeter involved a complete replacement of the photodetectors and readout electronics. Currently, the endcap portion of the calorimeter is complete, while the remaining portion of the detector will be completed next year. We are also developing new techniques to analyze the data from CMS. One such example is the application of modern machine learning techniques, such as complex neural networks, to identify top quark decays. This is used to enhance the sensitivity in an analysis searching for top squarks.
|Publisher||Department of Physics|
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