Using the Higgs to Probe Naturalness

The extreme sensitivity of the mass of the Higgs boson to quantum corrections from high mass states, makes it 'unnaturally' light in the standard model. This 'hierarchy problem' can be solved by symmetries, which predict new particles related, by the symmetry, to standard model fields. The Large Hadron Collider (LHC) can potentially discover these new particles, thereby finding the solution to the hierarchy problem. However, the dynamics of the Higgs boson is also sensitive to this new physics. We show that in many scenarios the Higgs can be a complementary and powerful probe of the hierarchy problem at the LHC and future colliders. If the top quark partners carry the color charge of the strong nuclear force, the production of Higgs pairs is affected. This effect is tightly correlated with single Higgs production, implying that only modest enhancements in di-Higgs production occur when the top partners are heavy. However, if the top partners are light, we show that di-Higgs production is a useful complementary probe to single Higgs production. We verify this result in the context of a simplified supersymmetric model. If the top partners do not carry color charge, their direct production is greatly reduced. Nevertheless, we show that such scenarios can be revealed through Higgs dynamics. We find that many color neutral frameworks leave observable traces in Higgs couplings, which, in some cases, may be the only way to probe these theories at the LHC. Some realizations of the color neutral framework also lead to exotic decays of the Higgs with displaced vertices. We show that these decays are so striking that the projected sensitivity for these searches, at hadron colliders, is comparable to that of searches for colored top partners. Taken together, these three case studies show the efficacy of the Higgs as a probe of naturalness.

STATISTICAL METHODS FOR ADVANCED ECONOMETRIC MODELS WITH APPLICATIONS TO VEHICLE HOLDING AND SPEED QUANTILES DISTRIBUTION

This dissertation proposes statistical methods to formulate, estimate and apply complex transportation models. Two main problems are part of the analyses conducted and presented in this dissertation. The first method solves an econometric problem and is concerned with the joint estimation of models that contain both discrete and continuous decision variables. The use of ordered models along with a regression is proposed and their effectiveness is evaluated with respect to unordered models. Procedure to calculate and optimize the log-likelihood functions of both discrete-continuous approaches are derived, and difficulties associated with the estimation of unordered models explained. Numerical approximation methods based on the Genz algortithm are implemented in order to solve the multidimensional integral associated with the unordered modeling structure. The problems deriving from the lack of smoothness of the probit model around the maximum of the log-likelihood function, which makes the optimization and the calculation of standard deviations very difficult, are carefully analyzed. A methodology to perform out-of-sample validation in the context of a joint model is proposed. Comprehensive numerical experiments have been conducted on both simulated and real data. In particular, the discrete-continuous models are estimated and applied to vehicle ownership and use models on data extracted from the 2009 National Household Travel Survey. The second part of this work offers a comprehensive statistical analysis of free-flow speed distribution; the method is applied to data collected on a sample of roads in Italy. A linear mixed model that includes speed quantiles in its predictors is estimated. Results show that there is no road effect in the analysis of free-flow speeds, which is particularly important for model transferability. A very general framework to predict random effects with few observations and incomplete access to model covariates is formulated and applied to predict the distribution of free-flow speed quantiles. The speed distribution of most road sections is successfully predicted; jack-knife estimates are calculated and used to explain why some sections are poorly predicted. Eventually, this work contributes to the literature in transportation modeling by proposing econometric model formulations for discrete-continuous variables, more efficient methods for the calculation of multivariate normal probabilities, and random effects models for free-flow speed estimation that takes into account the survey design. All methods are rigorously validated on both real and simulated data.