Home List of Titles The 2dF Galaxy Redshift Survey: the nature of the relative bias between galaxies of different spectral type
Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/42320
|Download PDF (author's final draft) (Adobe Acrobat PDF, -1 bytes)|
- The 2dF Galaxy Redshift Survey: the nature of the relative bias between galaxies of different spectral type
- Conway, Edward; Maddox, Stephen J.; Wild, Vivienne; Peacock, John A.; Hawkins, Edward; Norberg, P.; Madgwick, Darren; Baldry, Ivan K.; Baugh, Carlton; Bland-Hawthorn, Joss; Bridges, Terry J.; Cannon, Russell D.; Cole, Shaun; Colless, Matthew; Collins, Chris A.; Couch, Warrick J.; Dalton, Gavin B.; De Propris, Roberto; Driver, Simon P.; Efstathiou, George P.; Ellis, Richard S.; Frenk, Carlos S.; Glazebrook, Karl; Jackson, C. A.; Jones, Bryn; Lahav, Ofer; Lewis, Ian; Lumsden, Stuart L.; Percival, W.; Peterson, Bruce A.; Sutherland, William J.; Taylor, Keith
- We present an analysis of the relative bias between early- and late-type galaxies in the Two-degree Field Galaxy Redshift Survey (2dFGRS) – as defined by the η parameter of Madgwick et al., which quantifies the spectral type of galaxies in the survey. We calculate counts in cells for flux-limited samples of early- and late-type galaxies, using approximately cubical cells with sides ranging from 7 to 42 h−1 Mpc . We measure the variance of the counts in cells using the method of Efstathiou et al., which we find requires a correction for a finite volume effect equivalent to the integral constraint bias of the autocorrelation function. Using a maximum-likelihood technique we fit lognormal models to the one-point density distribution, and develop methods of dealing with biases in the recovered variances resulting from this technique. We then examine the joint density distribution function, f(δE, δL) , and directly fit deterministic bias models to the joint counts in cells. We measure a linear relative bias of ≈1.3, which does not vary significantly with ℓ. A deterministic linear bias model is, however, a poor approximation to the data, especially on small scales (ℓ≤ 28 h−1 Mpc) where deterministic linear bias is excluded at high significance. A power-law bias model with index b1≈ 0.75 is a significantly better fit to the data on all scales, although linear bias becomes consistent with the data for ℓ≳ 40 h−1 Mpc .
- Publication type
- Journal article
- Monthly Notices of the Royal Astronomical Society, Vol. 356, no. 2 (Jan 2005), pp. 456-474
- Publication year
- Counts; Density; Dependence; Distances; Evolution; Galaxies; Large-scale structure; Luminosity; Moments; Morphology; Redshifts; Statistics; Surveys; Universe
- Wiley-Blackwell Publishing
- Publisher URL
- Copyright © 2004 RAS.
- Full text
- Peer reviewed