Ze Chen M.Sc.

Ze Chen M.Sc.
  • PhD student, joined April 2024
  • Thesis Title: IBD cut selection in the JUNO experiment with real data

Contact:
  • Phone: +49 2461 61 4123
  • E-mail: ze.chen1_AT_outlook.com

Education

  • 2018-2022, M.Sc. in Physics
    Universität Hamburg, Germany
    Master Thesis: Tau reconstruction in CMS exploiting machine learning techniques

  • 2013-2017, B.Sc. in Physics
    Tongji University, China
    Bachelor Thesis: Formation and evolution of jet following impact on free surface

Research Activity

JUNO (present)

JUNO, a multipurpose neutrino physics experiment currently under construction in China, is expected to be completed and start data taking at the end of 2024. Its main detector component will be 20 kton of liquid scillator, which is designed to detect electron-neutrinos from nearby reactors. The main physics goal of JUNO is to determine the neutrino mass ordering and improve the precision of neutrino oscillation parameters to the order of subpercent magnitude with expected 6 years of data. JUNO will also be a perfect platform for detecting solar/atmospheric/geo neutrinos,and other exotic searches.

My work in JUNO will start from the tuning of IBD cut selection, using Monto-Karlo simulated data. The idea is following: JUNO can detect many signals a day, but only ~60 events are from Inverse-Beta-decay (IBD), which is weak interaction absorbing electron-antineutrinos. To remove most of the backgroud and increase signal/background ratio, it is essential to apply multiple cuts before oscillation parameter fitting. The IBD selection cuts are tested usually with a mixed dataset, involving IBD events and background from for example radioactive decays.

CMS (October 2021 - September 2022)

The Compact Muon Solenoid (CMS) is a detector situated along the Large Hadron Collider (LHC), which is currently the largest proton accelerator, located at the European Organization for Nuclear Research (CERN). As a general-purpose detector, CMS is engaged in a broad range of physics programme studying Standard Model (including Higgs Boson) and searching evidence for extended models. The produced particles from proton collisions pass through the detector per bunch crossing (25 ns), leaving a track or energy deposit, which are used to reconstruct physical events.

During my master thesis, I participated in the algorithm development of tau lepton reconstruction, more precisely, a machine-learning-based reconstruction algorithm. Tau leptons decay very fast and are only visible in CMS in terms of their decay products. In my algorithm, the decay modes of tau leptons are reconstructed, with the efficiency as good as the conventional algorithm used in CMS.

Talks

  • DPG 2022 meeting, Heidelberg, Germany, 20-25 March 2022 (online)
    Talk: "Tau reconstruction exploiting machine learning techniques at CMS"