Techniques for cosmological analysis of next generation low to mid-frequency radio data

  • Michael Tarr

Student thesis: Doctoral Thesis


For centuries, astronomical instrumentation has continued to develop and improve. This has led to the incredible precision seen in recent observations, with ever greater accuracy to come. From their measurements, cosmologists have produced a complex model of the Universe. This indicates that most of the energy in the Universe is in some “dark” form that does not interact directly with the electromagnetic spectrum. One of the greatest challenges of modern cosmology is the understanding of these dark components. In particular weak gravitational lensing has emerged as powerful tool for probing these parts of the cosmological model.

An area of observation which has undergone an accelerated improvement in recent years, is radio astronomy. The radio regime therefore, is an exciting source of new discoveries and further cosmological constraints. Several studies have shown that the lensing signal can be detected at radio wavelengths, while others have demonstrated the significant impact of combining the results from next-generation radio and optical telescopes. In order to fully realise the potential of lensing and other measurements for cosmology, a comprehensive understanding of the radio galaxy population is also required.

In this thesis, I consider the future of radio observations for precision cosmology and the required developments in analysis techniques. I aim to contribute to this progress by focusing specifically on: (i) Providing novel estimators for radio weak lensing measurements and (ii) Studying the redshifts and statistics of galaxy populations in the radio, using optical spectroscopy. Both of these areas will soon be the subject of large next-generation observations, in the form of the Square Kilometre Array (SKA) continuum surveys; and a joint experiment between a next-generation spectroscopy facility (WEAVE) for the William Herschel Telescope (WHT) and the LOw Frequency ARray (LOFAR).

Date of AwardMay 2018
Original languageEnglish
Awarding Institution
  • University of Portsmouth
SupervisorDavid Bacon (Supervisor)

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