- Title
- A Bayesian approach to tilted-ring modelling of galaxies
- Creator
- Maina, Eric Kamau
- Subject
- Bayesian statistical decision theory
- Subject
- Galaxies
- Subject
- Radio astronomy
- Subject
- TiRiFiC (Tilted Ring Fitting Code)
- Subject
- Neutral hydrogen
- Subject
- Spectroscopic data cubes
- Subject
- Galaxy parametrisation
- Date Issued
- 2020
- Date
- 2020
- Type
- text
- Type
- Thesis
- Type
- Masters
- Type
- MSc
- Identifier
- http://hdl.handle.net/10962/145783
- Identifier
- vital:38466
- Description
- The orbits of neutral hydrogen (H I) gas found in most disk galaxies are circular and also exhibit long-lived warps at large radii where the restoring gravitational forces of the inner disk become weak (Spekkens and Giovanelli 2006). These warps make the tilted-ring model an ideal choice for galaxy parametrisation. Analysis software utilizing the tilted-ring-model can be grouped into two and three-dimensional based software. Józsa et al. (2007b) demonstrated that three dimensional based software is better suited for galaxy parametrisation because it is affected by the effect of beam smearing only by increasing the uncertainty of parameters but not with the notorious systematic effects observed for two-dimensional fitting techniques. TiRiFiC, The Tilted Ring Fitting Code (Józsa et al. 2007b), is a software to construct parameterised models of high-resolution data cubes of rotating galaxies. It uses the tilted-ring model, and with that, a combination of some parameters such as surface brightness, position angle, rotation velocity and inclination, to describe galaxies. TiRiFiC works by directly fitting tilted-ring models to spectroscopic data cubes and hence is not affected by beam smearing or line-of-site-effects, e.g. strong warps. Because of that, the method is unavoidable as an analytic method in future Hi surveys. In the current implementation, though, there are several drawbacks. The implemented optimisers search for local solutions in parameter space only, do not quantify correlations between parameters and cannot find errors of single parameters. In theory, these drawbacks can be overcome by using Bayesian statistics, implemented in Multinest (Feroz et al. 2008), as it allows for sampling a posterior distribution irrespective of its multimodal nature resulting in parameter samples that correspond to the maximum in the posterior distribution. These parameter samples can be used as well to quantify correlations and find errors of single parameters. Since this method employs Bayesian statistics, it also allows the user to leverage any prior information they may have on parameter values.
- Format
- 137 pages
- Format
- Publisher
- Rhodes University
- Publisher
- Faculty of Science, Physics and Electronics
- Language
- English
- Rights
- Maina, Eric Kamau
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