Thesis

Development of High-Precision Timing Detector for the CMS experiment at HL-LHC

Details

  • Call:

    PT-CERN Call 2021/1

  • Academic Year:

    2021

  • Domain:

    Astrophysics

  • Supervisor:

    Joao Varela

  • Co-Supervisor:

    Michele Gallinaro

  • Institution:

    Universidade de Lisboa

  • Host Institution:

    Laboratório de Instrumentação e Física Experimental de Partículas

  • Abstract:

    The HL-LHC requires challenging new particle detectors in order for the experiment to handle the huge protons collision rate. An average of two hundred proton-proton collisions will occur at each bunch crossing, creating an enormous background to rare events in particular those where Higgs bosons are produced. In order to cope with this challenge, the precise measurement of the production time of the charged particles is required. The timing information provides a powerful discrimination of background. The current CMS detectors can achieve a time resolution of the order of 500-1000 picosecond, which is insufficient to maintain the sensitivity to rare physics processes in the HL-LHC era. A strong R&D program towards precise timing of charged particles is now underway aiming at a time resolution of the order of 30-50 picosecond, representing a huge improvement relative to the state-of-the-art. This work focuses on the development of a new Timing Detector based on LYSO scintillating crystals, silicon photomultipliers (SiPM) and dedicated ASIC microelectronics developed in Portugal. The technologies used are at the forefront of R&D in particle physics detectors. It is expected that the developed technologies would be transferred to commercial applications like Light Detection And Range (LiDAR) and Time-of-Flight Positron Emission Tomography. The program of work will integrate a broad range of topics in detector physics and technology, including evaluation of detector prototypes with particle beams at CERN, as well as simulation studies of the impact of the Timing Detector in the CMS physics program.