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Title: Source associations for the virtual observatory
Author: Taylor, E. L.
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2007
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This thesis presents investigations into different methods of associating astronomical sources detected at different wavelengths, and describes the development of a tool for AstroGrid to enable users to associate sources in a fully automated manner. We describe detailed investigation into the likelihood ratio method through the association of a population of far-infrared sources from the FIRBACK survey with optical counterparts from the INT Wide Field Survey. This is a challenging association problem since the far-infrared sources have a large positional error due to the poor resolution of the instrument and their relatively long wavelength. We compare two different variants of the likelihood ratio method in detail, and use the better one to derive optical counterparts for the far-infrared sources. The scientific benefits of associating multiwavelength data are illustrated through an investigation into the nature of the FIRBACK sources. These are identified with not only an optical counterpart but also with data at up to nine further wavelengths. Their properties are examined through the comparison of their observed spectral energy distributions with predictions from radiative transfer models which simulate the emission from both cirrus and starburst components. The far-infrared sources are found to be 80 per cent star-bursting galaxies with their starburst component at a high optical depth. It is common situation in astronomy to wish to investigate a source population for which we have no prior knowledge about the properties of the source counterparts expected at another wavelength, for example through observations with a new instrument. In such a case it is necessary to estimate the counterpart magnitude distribution to use the likelihood ratio association method. Since little was known about the FIRBACK sources prior to these investigations their optical magnitude distribution had to be estimated in order to assign them optical IDs. To alleviate this problem we have developed a new astronomical application of a machine learning technique known as the EM algorithm which is used in the field of informatics. This is able to ‘learn’ the source magnitude distribution iteratively. The algorithm is tested on the FIRBACK sources and also radio sources from the HI Parkes All-Sky Survey catalogue and is found to be a very effective association method.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available