Supersymmetry searches in the single lepton final state with the ATLAS detector

This thesis presents an analysis for the search of Supersymmetry with the ATLAS detector at the LHC. The final state with one lepton, several coloured particles and large missing transverse energy was chosen. Particular emphasis was placed on the optimization of the requirements for lepton identification. This optimization showed to be particularly useful when combining with multi-lepton selections. The systematic error associated with the higher order QCD diagrams in Monte Carlo production is given particular focus. Methods to verify and correct the energy measurement of hadronic showers are developed. Methods for the identification and removal of mismeasurements caused by the detector are found in the single muon and four jet environment are applied. A new detector simulation system is shown to provide good prospects for future fast Monte Carlo production. The analysis was performed for $35pb^{-1}$ and no significant deviation from the Standard Model is seen. Exclusion limits subchannel for minimal Supergravity. Previous limits set by Tevatron and LEP are extended.

A new information fusion method for land-use classification using high resolution satellite imagery

Satellite image classification involves designing and developing efficient image classifiers. With satellite image data and image analysis methods multiplying rapidly, selecting the right mix of data sources and data analysis approaches has become critical to the generation of quality land-use maps. In this study, a new postprocessing information fusion algorithm for the extraction and representation of land-use information based on high-resolution satellite imagery is presented. This approach can produce land-use maps with sharp interregional boundaries and homogeneous regions. The proposed approach is conducted in five steps. First, a GIS layer - ATKIS data - was used to generate two coarse homogeneous regions, i.e. urban and rural areas. Second, a thematic (class) map was generated by use of a hybrid spectral classifier combining Gaussian Maximum Likelihood algorithm (GML) and ISODATA classifier. Third, a probabilistic relaxation algorithm was performed on the thematic map, resulting in a smoothed thematic map. Fourth, edge detection and edge thinning techniques were used to generate a contour map with pixel-width interclass boundaries. Fifth, the contour map was superimposed on the thematic map by use of a region-growing algorithm with the contour map and the smoothed thematic map as two constraints. For the operation of the proposed method, a software package is developed using programming language C. This software package comprises the GML algorithm, a probabilistic relaxation algorithm, TBL edge detector, an edge thresholding algorithm, a fast parallel thinning algorithm, and a region-growing information fusion algorithm. The county of Landau of the State Rheinland-Pfalz, Germany was selected as a test site. The high-resolution IRS-1C imagery was used as the principal input data.