Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy

Astrophysics and cosmology are rich with data. The advent of wide-area digital cameras on large aperture telescopes has led to ever more ambitious surveys of the sky. Data volumes of entire surveys a decade ago can now be acquired in a single night and real-time analysis is often desired. Thus, modern astronomy requires big data know-how, in particular it demands highly efficient machine learning and image analysis algorithms. But scalability is not the only challenge: Astronomy applications touch several current machine learning research questions, such as learning from biased data and dealing with label and measurement noise. We argue that this makes astronomy a great domain for computer science research, as it pushes the boundaries of data analysis. In the following, we will present this exciting application area for data scientists. We will focus on exemplary results, discuss main challenges, and highlight some recent methodological advancements in machine learning and image analysis triggered by astronomical applications

Statistics of Natural Image Geometry

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Turbulence in Optical Flow Fields

In this thesis I will examine the relations between turbulence as found in physical systems and turbulence in optic flow fields. I will record some image sequences of a theoretically well understood turbulent physical system and from these image sequences I will examine the relations between the optic flow of the image sequences and the physical parameters of the turbulent system. I will examine the image sequences at different scales using the theory of linear Gaussian scale-space, because linear Gaussian scale-space has proven to be a valuable tool in digital image processing and because the physics of turbulence can be explained through scaling properties of the turbulent flow. From these analyses I hope to be able to describe the theoretical relations between turbulence in optic flow fields and turbulence in physical systems. I have obtained the permission to borrow and use equipment and experimental setup for the recording of the image sequences by Preben Alstrm and Mogens Levinse..