Search for Neutrinoless Double Beta Decay with EXO-200 and the Application of Deep Learning to Detector Simulation
Li, Shaolei
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https://hdl.handle.net/2142/115319
Description
Title
Search for Neutrinoless Double Beta Decay with EXO-200 and the Application of Deep Learning to Detector Simulation
Author(s)
Li, Shaolei
Issue Date
2021-12-22
Director of Research (if dissertation) or Advisor (if thesis)
Yang, Liang
Doctoral Committee Chair(s)
Perdekamp, Matthias Grosse
Committee Member(s)
Draper, Patrick I
MacDougall, Gregory
Department of Study
Physics
Discipline
Physics
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Physics
Language
eng
Abstract
The EXO-200 detector is designed to search for the neutrinoless double beta decay (0νββ) of 136Xe. Such a decay, if observed, would demonstrate the Majorana nature of neutrino; set the mass scale of the neutrino sector; and demonstrate lepton number non-conservation. The EXO- 200 detector and its successor, nEXO, use liquid Xenon time projection technology to perform the search. One important performance parameter of the detector is its energy resolution. In the first part of this work, we review the analysis work to improve the energy resolution and the status of the 0νββ search. This includes a description of the advanced analysis techniques used to maximize the energy resolution with improved charge channels, calibration ,and more precise Light-Maps. The second part of this work presents the current state of deep learning efforts towards fast simulations of the scintillation signals using Wasserstein Generative Adversarial Network (GAN) algorithms.
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