THESIS
2019
xviii, 103 pages : color illustrations ; 30 cm
Abstract
Despite its great success, the Standard Model of particle physics predicts the Higgs mass to
be unstable at quantum level, which is known as naturalness problem. Various theories to
address this problem have been proposed in last several decades, e.g. supersymmetry. In
this thesis, we explore several aspects of this topic, ranging from theory study to collider
analysis and to the design of data analysis algorithm. We first explore an ultraviolet
extension of Twin Higgs model in which the radial mode of global symmetry breaking
is itself a pseudo-Nambu-Goldstone Boson. This “turtle” structure raises the scale of
new colored states, in exchange for additional states in the Higgs sector, making multiple
scalars the definitive signature of naturalness in this context. We then introd...[
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Despite its great success, the Standard Model of particle physics predicts the Higgs mass to
be unstable at quantum level, which is known as naturalness problem. Various theories to
address this problem have been proposed in last several decades, e.g. supersymmetry. In
this thesis, we explore several aspects of this topic, ranging from theory study to collider
analysis and to the design of data analysis algorithm. We first explore an ultraviolet
extension of Twin Higgs model in which the radial mode of global symmetry breaking
is itself a pseudo-Nambu-Goldstone Boson. This “turtle” structure raises the scale of
new colored states, in exchange for additional states in the Higgs sector, making multiple
scalars the definitive signature of naturalness in this context. We then introduce new
channels to search for extended Higgs sector which is a landmark of physics beyond
the SM including theories of naturalness, at the LHC and next-generation pp-colider.
Facilitated with the Boost-Decision-Tree method, we show that LHC has a potential to
probe a Higgs sector up to O(1) TeV and a future 100TeV pp-collider close to O(10) TeV in
the benchmark of the Minimal Supersymmetric Standard Model. We further explore the
application of unsupervised learning method, known as novelty detection, to data analysis
in particle physics. This method is model-independent and hence is complementary to
supervised learning. With a set of density-based novelty evaluators developed in this
study and a deep neural network based on autoencoder, we demonstrate the potential
capability of novelty detection in exploring new physics at colliders.
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