A search for the Standard Model Higgs boson in proton-antiproton collisions with center-of-mass energy 1.96 TeV at the Tevatron is presented in this dissertation. The process of interest is the associated production of W boson and Higgs boson, with the W boson decaying leptonically and the Higgs boson decaying into a pair of bottom quarks. The dataset in the analysis is accumulated by the D0 detector from April 2002 to April 2008 and corresponding to an integrated luminosity of 2.7 fb{sup -1}. The events are reconstructed and selected following the criteria of an isolated lepton, missing transverse energy and two jets. The D0 Neural Network b-jet identification algorithm is further used to discriminate b jets from light jets. A multivariate analysis combining Matrix Element and Neural Network methods is explored to improve the Higgs boson signal significance. No evidence of the Higgs boson is observed in this analysis. In consequence, an observed (expected) limit on the ratio of {sigma} (p{bar p} {yields} WH) x Br (H {yields} b{bar b}) to the Standard Model prediction is set to be 6.7 (6.4) at 95% C.L. for the Higgs boson with a mass of 115 GeV.

Knowledge discovery from large and complex collections of today's scientific datasets is a challenging task. With the ability to measure and simulate more processes at increasingly finer spatial and temporal scales, the increasing number of data dimensions and data objects is presenting tremendous challenges for data analysis and effective data exploration methods and tools. Researchers are overwhelmed with data and standard tools are often insufficient to enable effective data analysis and knowledge discovery. The main objective of this thesis is to provide important new capabilities to accelerate scientific knowledge discovery form large, complex, and multivariate scientific data. The research covered in this thesis addresses these scientific challenges using a combination of scientific visualization, information visualization, automated data analysis, and other enabling technologies, such as efficient data management. The effectiveness of the proposed analysis methods is demonstrated via applications in two distinct scientific research fields, namely developmental biology and high-energy physics.Advances in microscopy, image analysis, and embryo registration enable for the first time measurement of gene expression at cellular resolution for entire organisms. Analysis of high-dimensional spatial gene expression datasets is a challenging task. By integrating data clustering and visualization, analysis of complex, time-varying, spatial gene expression patterns and their formation …