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published_or_final_version / Mechanical Engineering / Master / Master of Science in Engineering

Neutrino telescopes open a new observational window on the universe. Neutrino interactions in these detectors can give rise to a combination of electromagnetic cascades, hadronic cascades and long range muons. Cerenkov radiation from these products is detected by the neutrino telescope. In this thesis the observational signatures associated with various neutrino-nucleon interaction products are investigated. Cerenkov radiation is emitted at a distinctive angle, about 40o in ice. The maximum number of optical photons that can be produced per unit charged tracklength is calculated to be 562 photons cm−1. The simulation programs Pythia and GEANT are used to study neutrino interactions using ice as the medium. The production of tau from the tau neutrino interaction is considered and it is found that the Cerenkov angle from tau is not distinctive at low energies, due to its lifetime tau decays before travelling an observable distance. The energy required for a tau neutrino to produce a sharp tau Cerenkov signal is on the order of 1 PeV. In a high energy electron neutrino interaction the resulting hadronic cascade contains high energy pions and kaons. These particles decay, often producing muons that are also high energy and therefore long range. Due to the muons travelling faster than the local speed of light in ice, their signal may be received by the detector earlier than the signal resulting from the event that created the muon. This can complicate the reconstruction of electron neutrino events.

Thesis (Ph. D.)--University of Queensland, 2002. / Includes bibliographical references.

Thesis--M. Sc. (Eng.), University of Hong Kong. / Mimeographed.

Neutrino telescopes open a new observational window on the universe. Neutrino interactions in these detectors can give rise to a combination of electromagnetic cascades, hadronic cascades and long range muons. Cerenkov radiation from these products is detected by the neutrino telescope. In this thesis the observational signatures associated with various neutrino-nucleon interaction products are investigated. Cerenkov radiation is emitted at a distinctive angle, about 40o in ice. The maximum number of optical photons that can be produced per unit charged tracklength is calculated to be 562 photons cm−1. The simulation programs Pythia and GEANT are used to study neutrino interactions using ice as the medium. The production of tau from the tau neutrino interaction is considered and it is found that the Cerenkov angle from tau is not distinctive at low energies, due to its lifetime tau decays before travelling an observable distance. The energy required for a tau neutrino to produce a sharp tau Cerenkov signal is on the order of 1 PeV. In a high energy electron neutrino interaction the resulting hadronic cascade contains high energy pions and kaons. These particles decay, often producing muons that are also high energy and therefore long range. Due to the muons travelling faster than the local speed of light in ice, their signal may be received by the detector earlier than the signal resulting from the event that created the muon. This can complicate the reconstruction of electron neutrino events.

Thesis (M.S.)--Humboldt State University, 2009. / Includes bibliographical references (leaves 85-93). Also available via Humboldt Digital Scholar.

Cascades are a popular construct to observe and study information propagation (or diffusion) in social media such as Twitter and are defined using notions of influence, activity, or discourse commonality (e.g., hashtags). While these notions of cascades lead to different perspectives, primarily cascades are modeled as trees. We argue in this thesis an alternative viewpoint of cascades as forests (of trees) which yields a richer vocabulary of features to understand information propagation. We propose to develop a framework to extract forests and analyze their growth by studying their evolution at the tree-level and at the node-level. Furthermore, we outline four different problems that use the forest framework. First, we show that such forests of information cascades can be used to design counter-contagion algorithms to disrupt the spread of negative campaigns or rumors. Secondly, we demonstrate how such forests of information cascades can give us a rich set of features (structural and temporal), which can be used to forecast information flow. Thirdly, we argue that cascades modeled as forests can help us glean social network sensors to detect future contagious outbreaks that occur in the social network. To conclude, we show preliminary results of an approach - a generative model, that can describe information cascades modeled as forests and can generate synthetic cascades with empirical properties mirroring cascades extracted from Twitter. / Ph. D. / How do memes spread on blogs? How and when does a hashtag become popular? Can we predict viral content? This thesis answers such questions by analyzing information dissemination in social media. Only few years ago the goal of modeling large social and technological systems would have been unattainable. However, in less than a decade the world wide web has transformed from a large static library that people only browse into a vast information resource where people interact with each other. Through the emergence of online social networking and social media, daily activities of hundreds of millions of people are migrating to the Web. Today the Web is a “sensor” that captures the pulse of human behavior: what we are thinking, what we are doing, and what we know. Moreover, social media activity has become precursors to several events, particularly disruptive ones like protests, strike, and “occupy” events. Therefore, analyzing and forecasting the emergence of such activity is an important social research problem. This thesis presents analytical and predictive models that can predict and detect bursts of activity in social media like Twitter. We also provide algorithmic tools that can effectively quell the spread of a rumor, predict viral content, and allow scientists to synthetically simulate such events computationally. The achievement of the thesis is to arm social scientists with tools that can assist in understanding some aspects of online social behavior.

Thesis (M.S.)--University of Delaware, 2009. / Principal faculty advisor: Michael A. O'Neal, Dept. of Geography. Includes bibliographical references.