We use the 2dF QSO Redshift Survey, to investigate whether QSOs are detectably gravitationally lensed. Lensing could magnify and distort light from QSOs, influencing QSO numbers near galaxies, which trace structure in our Universe. Following Boyle, Fong & Shanks (1988), we find a 3σ anti-correlation between QSOs and galaxy groups of strength W (_gg)(< 10') = -0.049. We limit absorption by dust in groups to A(_B) < 0.04 mag. To explain the anti-correlation by dust would need Ab ≈ 0.2 mag. We demonstrate that if the dearth of QSOs around groups is due to statistical lensing, more mass would be required in groups than Ω(_m) = 0.3 models suggest. We use a mock catalogue to test how many of our "2D" galaxy groups, which are detected using angular information, are associated in redshift-space. We then utilise 2dF Galaxy Redshift Survey groups, which are selected to trace dark matter haloes, to test the hypothesis that there is more mass in groups than Ωr(_m) = 0.3 models suggest, finding we cannot discount a lensing mass of 2dFGRS groups that is consistent with ACDM. We find QSOs and galaxies are also anti-correlated at the 3σ level, with strength w(< 10’) = -0.007 and use stars as a control sample to rule out observational systematics as a cause. By measuring QSO colours as a function of QSO-galaxy separation, we argue that obscuration by dust in galaxies could explain at most 30-40 per cent of the anti correlation. We show that if the anti-correlation is due to lensing, galaxies would be anti-biased [b ~ 0.05) on small scales. We discuss two surveys carried out to count faint QSOs, which newly identify 160 QSOs. We calculate that the faint-end QSO number-counts have a slope of 0.29 ± 0.03. Finally, we use our faint QSO data, to estimate that ~ 85(75) per cent of g < 21.15 (≥ 21.15) candidates targeted by the 2dFSDSS survey will be QSOs.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:274749 |
| Date | January 2003 |
| Creators | Myers, Adam David |
| Publisher | Durham University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://etheses.dur.ac.uk/3686/ |
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