This thesis unifies several studies, which all are dedicated to the subject of statistical data
analysis in radio astronomy and radio astrophysics.
Radio astronomy, like astronomy as a whole, has undergone a remarkable development in the past twenty years in introducing new instruments and technologies. New telescopes like the upgraded VLA, LOFAR, or the SKA and its pathfinder missions offer unprecedented sensitivities, previously uncharted frequency domains and unmatched survey capabilities.
Many of these have the potential to significantly advance the science of radio astrophysics and cosmology on all scales, from solar and stellar
physics, Galactic astrophysics and cosmic magnetic fields, to Galaxy cluster astrophysics
and signals from the epoch of reionization.
Since then, radio data analysis, calibration and imaging techniques have entered a similar phase of new development to push the boundaries and adapt the field to the new instruments and scientific opportunities.
This thesis contributes to these greater developments in two specific subjects, radio interferometric imaging and cosmic magnetic field statistics.
Throughout this study, different data analysis techniques are presented and employed
in various settings, but all can be summarized under the broad term of statistical infer-
ence. This subject encompasses a huge variety of statistical techniques, developed to solve
problems in which deductions have to be made from incomplete knowledge, data or measurements. This study focuses especially on Bayesian inference methods that make use of a subjective definition of probabilities, allowing for the expression of probabilities and statistical knowledge prior to an actual measurement.
The thesis contains two different sets of application for such techniques. First, situations
where a complicated, and generally ill-posed measurement problem can be approached by
assuming a statistical signal model prior to infer the desired measured variable. Such
a problem very often is met should the measurement device take less data then needed
to constrain all degrees of freedom of the problem. The principal case investigated in
this thesis is the measurement problem of a radio interferometer, which takes incomplete
samples of the Fourier transformed intensity of the radio emission in the sky, such that it is
impossible to exactly recover the signal. The new imaging algorithm RESOLVE is presented,
optimal for extended radio sources. A first showcase demonstrates the performance of the
new technique on real data. Further, a new Bayesian approach to multi-frequency radio
interferometric imaging is presented and integrated into RESOLVE.
The second field of application are astrophysical problems, in which the inherent stochas-
tic nature of a physical process demands a description, where properties of physical quanti-
ties can only be statistically estimated. Astrophysical plasmas for instance are very often in a turbulent state, and thus governed by statistical hydrodynamical laws. Two studies are presented that show how properties of turbulent plasma magnetic fields can be inferred from radio observations.
| Identifer | oai:union.ndltd.org:MUENCHEN/oai:edoc.ub.uni-muenchen.de:17745 |
| Date | 10 April 2014 |
| Creators | Junklewitz, Henrik |
| Publisher | Ludwig-Maximilians-Universität München |
| Source Sets | Digitale Hochschulschriften der LMU |
| Detected Language | English |
| Type | Dissertation, NonPeerReviewed |
| Format | application/pdf |
| Relation | http://edoc.ub.uni-muenchen.de/17745/ |
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