High Energy Physics Seminar - Miaoyuan Liu (FermiLab) - Using Electroweak Bosons and the Higgs Boson to Probe New Physics at the LHC and Upgrades of the CMS Detector to Maximize the Chances of Such a Discovery

Mia Liu (FermiLab) 2/11/19 High Energy Physics seminar speaker
February 11, 2019
3:30 pm - 4:30 pm
4138 Physics Research Building

Date Range
2019-02-11 15:30:00 2019-02-11 16:30:00 High Energy Physics Seminar - Miaoyuan Liu (FermiLab) - Using Electroweak Bosons and the Higgs Boson to Probe New Physics at the LHC and Upgrades of the CMS Detector to Maximize the Chances of Such a Discovery The SM describes the building blocks of matter and their interactions. It has been tested extensively with experimental data and found to be incredibly successful in describing nature. Discovering the Higgs boson in 2012 at the LHC completed the picture of the SM. The LHC is at the forefront of directly searching for new physics which is Beyond-Standard-Model (BSM), and I will discuss searches for supersymmetric partners of the electroweak bosons, as well as measurement of an extremely rare process with three WWW bosons as stringent tests of the SM. I will also discuss the recently completed CMS Phase-1 pixel upgrade and the R&D studies towards solving the future trigger and computing challenges at CMS using innovative machine learning approaches. 4138 Physics Research Building America/New_York public

The SM describes the building blocks of matter and their interactions. It has been tested extensively with experimental data and found to be incredibly successful in describing nature. Discovering the Higgs boson in 2012 at the LHC completed the picture of the SM. The LHC is at the forefront of directly searching for new physics which is Beyond-Standard-Model (BSM), and I will discuss searches for supersymmetric partners of the electroweak bosons, as well as measurement of an extremely rare process with three WWW bosons as stringent tests of the SM. I will also discuss the recently completed CMS Phase-1 pixel upgrade and the R&D studies towards solving the future trigger and computing challenges at CMS using innovative machine learning approaches.