In this thesis, I investigate the spatial distribution of radio sources, and quantify their clustering strength over a range of redshifts, up to z _ 2:2, using various forms of the correlation function measured with data from several multi-wavelength surveys. I present the optical spectra of 30 radio AGN (S1:4 > 100 mJy) in the GAMA/H-ATLAS fields, for which emission line redshifts could be deduced, from observations of 79 target sources with the EFOSC2 spectrograph on the NTT. The mean redshift of these sources is z = 1:2; 12 were identified as quasars (40 per cent), and 6 redshifts (out of 24 targets) were found for AGN hosts to multiple radio components. While obtaining spectra for hosts of these multi-component sources is possible, their lower success rate highlights the difficulty in acheiving a redshift-complete radio sample. Taking an existing spectroscopic redshift survey (GAMA) and radio sources from the FIRST survey (S1:4 > 1 mJy), I then present a cross-matched radio sample with 1,635 spectroscopic redshifts with a median value of z = 0:34. The spatial correlation function of this sample is used to find the redshiftspace (s0) and real-space correlation lengths (r0 _ 8:2 h 1Mpc), and a mass bias of _1.9. Insight into the redshift-dependence of these quantities is gained by using the angular correlation function and Limber inversion to measure the same spatial clustering parameters. Photometric redshifts from SDSS/UKIDSS are incorporated to produce a larger matched radio sample at z ' 0:48 (and low- and high-redshift subsamples at z ' 0:30 and z ' 0:65), while their redshift distribution is subtracted from that taken from the SKADS radio simulations to estimate the redshift distribution of the remaining unmatched sources (z ' 1:55). The observed bias evolution over this redshift range is compared with model predictions based on the SKADS simulations, with good agreement at low redshift. The bias found at high redshift significantly exceeds these predictions, however, suggesting a more massive population of galaxies than expected, either due to the relative proportions of different radio sources, or a greater typical halo mass for the high-redshift sources. Finally, the reliance on a model redshift distribution to reach to higher redshifts is removed, as the angular cross-correlation function is used with deep VLA data (S1:4 > 90 _Jy) and optical/IR data from VIDEO/CFHTLS (Ks < 23:5) over 1 square degree. With high-quality photometric redshifts up to z _ 4, and a high signal-to-noise clustering measurement (due to the _100,000 Ks-selected galaxies), I am able to find the bias of a matched sample of only 766 radio sources (as well as of v vi the VIDEO sources), divided into 4 redshift bins reaching a median bias at z ' 2:15. Again, at high redshift, the measured bias appears to exceed the prediction made from the SKADS simulations. Applying luminosity cuts to the radio sample at L > 1023 WHz 1 and higher (removing any non-AGN sources), I find a bias of 8–10 at z _ 1:5, considerably higher than for the full sample, and consistent with the more numerous FRI AGN having similar mass to the FRIIs (M _ 1014 M_), contrary to the assumptions made in the SKADS simulations. Applying this adjustment to the model bias produces a better fit to the observations for the FIRST radio sources cross-matched with GAMA/SDSS/UKIDSS, as well as for the high-redshift radio sources in VIDEO. Therefore, I have shown that we require a more robust model of the evolution of AGN, and their relation to the underlying dark matter distribution. In particular, understanding these quantities for the abundant FRI population is crucial if we are to use such sources to probe the cosmological model as has been suggested by a number of authors (e.g. Raccanelli et al., 2012; Camera et al., 2012; Ferramacho et al., 2014).
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:638066 |
| Date | January 2015 |
| Creators | Lindsay, Samuel Nathan |
| Publisher | University of Hertfordshire |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/2299/15361 |
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