Software tools and efficient algorithms for the feature detection, feature tracking, event localization, and visualization of large sets of atmospheric data

Data sets describing the state of the earth's atmosphere are of great importance in the atmospheric sciences. Over the last decades, the quality and sheer amount of the available data increased significantly, resulting in a rising demand for new tools capable of handling and analysing these large, multidimensional sets of atmospheric data. The interdisciplinary work presented in this thesis covers the development and the application of practical software tools and efficient algorithms from the field of computer science, aiming at the goal of enabling atmospheric scientists to analyse and to gain new insights from these large data sets. For this purpose, our tools combine novel techniques with well-established methods from different areas such as scientific visualization and data segmentation. In this thesis, three practical tools are presented. Two of these tools are software systems (Insight and IWAL) for different types of processing and interactive visualization of data, the third tool is an efficient algorithm for data segmentation implemented as part of Insight.Insight is a toolkit for the interactive, three-dimensional visualization and processing of large sets of atmospheric data, originally developed as a testing environment for the novel segmentation algorithm. It provides a dynamic system for combining at runtime data from different sources, a variety of different data processing algorithms, and several visualization techniques. Its modular architecture and flexible scripting support led to additional applications of the software, from which two examples are presented: the usage of Insight as a WMS (web map service) server, and the automatic production of a sequence of images for the visualization of cyclone simulations. The core application of Insight is the provision of the novel segmentation algorithm for the efficient detection and tracking of 3D features in large sets of atmospheric data, as well as for the precise localization of the occurring genesis, lysis, merging and splitting events. Data segmentation usually leads to a significant reduction of the size of the considered data. This enables a practical visualization of the data, statistical analyses of the features and their events, and the manual or automatic detection of interesting situations for subsequent detailed investigation. The concepts of the novel algorithm, its technical realization, and several extensions for avoiding under- and over-segmentation are discussed. As example applications, this thesis covers the setup and the results of the segmentation of upper-tropospheric jet streams and cyclones as full 3D objects. Finally, IWAL is presented, which is a web application for providing an easy interactive access to meteorological data visualizations, primarily aimed at students. As a web application, the needs to retrieve all input data sets and to install and handle complex visualization tools on a local machine are avoided. The main challenge in the provision of customizable visualizations to large numbers of simultaneous users was to find an acceptable trade-off between the available visualization options and the performance of the application. Besides the implementational details, benchmarks and the results of a user survey are presented.

Efficient algorithms for NLO QCD event generators

In this thesis we present techniques that can be used to speed up the calculation of perturbative matrix elements for observables with many legs ($n = 3, 4, 5, 6, 7, ldots$). We investigate several ways to achieve this, including the use of Monte Carlo methods, the leading-color approximation, numerically less precise but faster operations, and SSE-vectorization. An important idea is the use of enquote{random polarizations} for which we derive subtraction terms for the real corrections in next-to-leading order calculations. We present the effectiveness of all these methods in the context of electron-positron scattering to $n$ jets, $n$ ranging from two to seven.

Probing quantum chromodynamics with the ATLAS detector : charged-particle event shape variables and the dijet cross-section

Die Quantenchromodynamik ist die zugrundeliegende Theorie der starken Wechselwirkung und kann in zwei Bereiche aufgeteilt werden. Harte Streuprozesse, wie zum Beispiel die Zwei-Jet-Produktion bei hohen invarianten Massen, können störungstheoretisch behandelt und berechnet werden. Bei Streuprozessen mit niedrigen Impulsüberträgen hingegen ist die Störungstheorie nicht mehr anwendbar und phänemenologische Modelle werden für Vorhersagen benutzt. Das ATLAS Experiment am Large Hadron Collider am CERN ermöglicht es, QCD Prozesse bei hohen sowie niedrigen Impulsüberträgen zu untersuchen. In dieser Arbeit werden zwei Analysen vorgestellt, die jeweils ihren Schwerpunkt auf einen der beiden Regime der QCD legen:rnDie Messung von Ereignisformvariablen bei inelastischen Proton--Proton Ereignissen bei einer Schwerpunktsenergie von $sqrt{s} = unit{7}{TeV}$ misst den transversalen Energiefluss in hadronischen Ereignissen. rnDie Messung des zweifachdifferentiellen Zwei-Jet-Wirkungsquerschnittes als Funktion der invarianten Masse sowie der Rapiditätsdifferenz der beiden Jets mit den höchsten Transversalimpulsen kann genutzt werden um Theorievorhersagen zu überprüfen. Proton--Proton Kollisionen bei $sqrt{s} = unit{8}{TeV}$, welche während der Datennahme im Jahr 2012 aufgezeichnet wurden, entsprechend einer integrierten Luminosität von $unit{20.3}{fb^{-1}}$, wurden analysiert.rn