Abstract
Despite its great success, the Standard Model (SM) predicts the Higgs mass to be unstable at quantum level, which is the so-called naturalness problem. Various models to address this problem have been proposed for decades. However, these traditional wisdoms are being challenged by either experimental measurements (supersymmetry and composite Higgses) or questioned by the ability to stabilize the Higgs mass up to a higher scale (Twin Higgs). In this thesis, we first explore an ultraviolet extension of Twin Higgs model in which the radial mode of twin symmetry breaking is itself a pseudo-goldstone boson. This “turtle” structure raises the most relevant degree of freedom (new colored particles) for naturalness in exchange for additional states in the Higgs sector, making multiple Higgs-like scalars the definitive signature of naturalness in this context. We then develop strategies to search for the extended Higgs sectors at the LHC and a next-generation ppcolider based on the decoupled Minimally Supersymmetric Model (MSSM), a landmark of models motivated by naturalness. We propose to search in channels with associated Higgs productions, with the neutral and charged Higgs further decaying in tt and tb, respectively. We show that LHC will be able to probe the whole parameter space for Higgs masses of O(1) TeV and a future 100TeV pp-collider of O(10) TeV. Complementary to the supervised learning method as used for heavy Higgs search, we further explore the novelty detection method for analyzing data model-independently. With a set of density-based novelty evaluators proposed and autoencoder-based deep neural network, we demonstrate the potential role of novelty detection in collider physics.