The distribution of red and blue galaxies in groups: an empirical test of the halo model

The popular halo model predicts that the power spectrum of the galaxy fluctuations is simply the sum of the large scale linear halo-halo power spectrum and the weighted power spectrum of the halo profile. Previous studies have derived halo parameters from the observed galaxy correlation function. Here we test the halo model directly for self-consistency with a minimal set of theoretical assumptions by utilising the 2dF Galaxy Redshift Survey (2dFGRS). We derive empirically the halo occupation and galaxy radial distributions in the haloes of the 2dF Percolation-Inferred Galaxy Group (2PIGG) catalogue. The mean halo occupation number is found to be well-fitted by a power-law, ~ M^b, at high masses, with b = 1.05, 0.88, 0.99 for red, blue and all galaxies respectively (with 1-sigma errors of 15-19%). We find that the truncated NFW profile provides a good fit to the galaxy radial distributions, with concentration parameters c=3.9, 1.3, 2.4 for red, blue and all galaxies respectively (with 1-sigma errors of 8-15%). Adding the observed linear power spectrum to these results, we compare these empirical predictions of the halo model with the observed correlation functions for these same 2dF galaxy populations. We conclude that subject to some fine tuning it is an acceptable model for the two-point correlations. Our analysis also explains why the correlation function slope of the red galaxies is steeper than that of the blue galaxies. It is mainly due to the number of red and blue galaxies per halo, rather than the radial distribution within the haloes of the two galaxy species.Comment: 15 pages, 15 figures. MNRAS accepted version. Adds appx. on profile fitting; now use truncated NF

ANNz: estimating photometric redshifts using artificial neural networks

We introduce ANNz, a freely available software package for photometric redshift estimation using Artificial Neural Networks. ANNz learns the relation between photometry and redshift from an appropriate training set of galaxies for which the redshift is already known. Where a large and representative training set is available ANNz is a highly competitive tool when compared with traditional template-fitting methods. The ANNz package is demonstrated on the Sloan Digital Sky Survey Data Release 1, and for this particular data set the r.m.s. redshift error in the range 0 < z < 0.7 is 0.023. Non-ideal conditions (spectroscopic sets which are small, or which are brighter than the photometric set for which redshifts are required) are simulated and the impact on the photometric redshift accuracy assessed.Comment: 6 pages, 6 figures. Replaced to match version accepted by PASP (minor changes to original submission). The ANNz package may be obtained from http://www.ast.cam.ac.uk/~aa

Excess Clustering on Large Scales in the MegaZ DR7 Photometric Redshift Survey

We observe a large excess of power in the statistical clustering of luminous red galaxies in the photometric SDSS galaxy sample called MegaZ DR7. This is seen over the lowest multipoles in the angular power spectra C-l in four equally spaced redshift bins between 0: 45 <= z <= 0: 65. However, it is most prominent in the highest redshift band at similar to 4 sigma and it emerges at an effective scale k less than or similar to 0: 01 h Mpc(-1). Given that MegaZ DR7 is the largest cosmic volume galaxy survey to date (3.3(Gpch(-1))(3)) this implies an anomaly on the largest physical scales probed by galaxies. Alternatively, this signature could be a consequence of it appearing at the most systematically susceptible redshift. There are several explanations for this excess power that range from systematics to new physics. We test the survey, data, and excess power, as well as possible origins

Empirical Photometric Redshifts of Luminous Red Galaxies and Clusters in SDSS

In this work I discuss the necessary steps for deriving photometric redshifts for luminous red galaxies (LRGs) and galaxy clusters through simple empirical methods. The data used is from the Sloan Digital Sky Survey (SDSS). I show that with three bands only ({\it gri}) it is possible to achieve results as accurate as the ones obtained by other techniques, generally based on more filters. In particular, the use of the $(g-i)$ color helps improving the final redshifts (especially for clusters), as this color monotonically increases up to $z \sim 0.8$. For the LRGs I generate a catalog of $\sim 1.5$ million objects at $z < 0.70$. The accuracy of this catalog is $\sigma = 0.027$ for $z \le 0.55$ and $\sigma = 0.049$ for $0.55 < z \le 0.70$. The photometric redshift technique employed for clusters is independent of a cluster selection algorithm. Thus, it can be applied to systems selected by any method or wavelength, as long as the proper optical photometry is available. When comparing the redshift listed in literature to the photometric estimate, the accuracy achieved for clusters is $\sigma = 0.024$ for $z \le 0.30$ and $\sigma = 0.037$ for $030 < z \le 0.55$. However, when considering the spectroscopic redshift as the mean value of SDSS galaxies on each cluster region, the accuracy is at the same level as found by other authors: $\sigma = 0.011$ for $z \le 0.30$ and $\sigma = 0.016$ for $030 < z \le 0.55$. The photometric redshift relation derived here is applied to thousands of cluster candidates selected elsewhere. I have also used galaxy photometric redshifts available in SDSS to identify groups in redshift space and then compare the redshift peak of the nearest group to each cluster redshift (ABRIDGED).Comment: 14 pages, 6 figures. Accepted to MNRAS. Minor changes in response to referee repor

Predicting spectral features in galaxy spectra from broad-band photometry

We explore the prospects of predicting emission line features present in galaxy spectra given broad-band photometry alone. There is a general consent that colours, and spectral features, most notably the 4000 A break, can predict many properties of galaxies, including star formation rates and hence they could infer some of the line properties. We argue that these techniques have great prospects in helping us understand line emission in extragalactic objects and might speed up future galaxy redshift surveys if they are to target emission line objects only. We use two independent methods, Artifical Neural Neworks (based on the ANNz code) and Locally Weighted Regression (LWR), to retrieve correlations present in the colour N-dimensional space and to predict the equivalent widths present in the corresponding spectra. We also investigate how well it is possible to separate galaxies with and without lines from broad band photometry only. We find, unsurprisingly, that recombination lines can be well predicted by galaxy colours. However, among collisional lines some can and some cannot be predicted well from galaxy colours alone, without any further redshift information. We also use our techniques to estimate how much information contained in spectral diagnostic diagrams can be recovered from broad-band photometry alone. We find that it is possible to classify AGN and star formation objects relatively well using colours only. We suggest that this technique could be used to considerably improve redshift surveys such as the upcoming FMOS survey and the planned WFMOS survey.Comment: 10 pages 7 figures summitted to MNRA

Implementation of PhotoZ under Astro-WISE - A photometric redshift code for large datasets

We describe the implementation of the PhotoZ code in the framework of the Astro-WISE package and as part of the Photometric Classification Server of the PanSTARRS pipeline. Both systems allow the automatic measurement of photometric redshifts for the millions of objects being observed in the PanSTARRS project or expected to be observed by future surveys like KIDS, DES or EUCLID.Comment: Accepted for publication in topical issue of Experimental Astronomy on Astro-WISE information system, references update

Cosmic shear requirements on the wavelength-dependence of telescope point spread functions

Cosmic shear requires high precision measurement of galaxy shapes in the presence of the observational Point Spread Function (PSF) that smears out the image. The PSF must therefore be known for each galaxy to a high accuracy. However, for several reasons, the PSF is usually wavelength dependent, therefore the differences between the spectral energy distribution of the observed objects introduces further complexity. In this paper we investigate the effect of the wavelength-dependence of the PSF, focusing on instruments in which the PSF size is dominated by the diffraction-limit of the telescope and which use broad-band filters for shape measurement. We first calculate biases on cosmological parameter estimation from cosmic shear when the stellar PSF is used uncorrected. Using realistic galaxy and star spectral energy distributions and populations and a simple three-component circular PSF we find that the colour-dependence must be taken into account for the next generation of telescopes. We then consider two different methods for removing the effect (i) the use of stars of the same colour as the galaxies and (ii) estimation of the galaxy spectral energy distribution using multiple colours and using a telescope model for the PSF. We find that both of these methods correct the effect to levels below the tolerances required for per-cent level measurements of dark energy parameters. Comparison of the two methods favours the template-fitting method because its efficiency is less dependent on galaxy redshift than the broad-band colour method and takes full advantage of deeper photometry.Comment: 10 pages, 8 figures, version accepted for publication in MNRA

The Angular Power Spectra of Photometric SDSS LRGs

We construct new galaxy angular power spectra based on the extended, updated and final SDSS II Luminous Red Galaxy (LRG) photometric redshift survey: MegaZ DR7. Encapsulating 7746 deg^{2} we utilise 723,556 photometrically determined LRGs between 0.45 < z < 0.65 in a 3.3 (Gpc h^{-1})^3 spherical harmonic analysis of the galaxy distribution. By combining four photometric redshift bins we find preliminary parameter constraints of f_{b} = \Omega_{b}/\Omega_{m} = 0.173 +/- 0.046 and \Omega_{m} = 0.260 +/- 0.035 assuming H_{0} = 75 km s^{-1} Mpc^{-1}, n_{s}=1 and \Omega_{k} = 0. These limits are consistent with the CMB and the previous data release (DR4). The C_{\ell} are sensitive to redshift space distortions and therefore we also recast our constraints into a measurement of \beta ~ \Omega_{m}^{0.55}/b in different redshift shells. The robustness of these power spectra with respect to a number of potential systematics such as extinction, photometric redshift and ANNz training set extrapolation are examined. The latter includes a cosmological comparison of available photometric redshift estimation codes where we find excellent agreement between template and empirical estimation methods. MegaZ DR7 represents a methodological prototype to next generation surveys such as the Dark Energy Survey (DES) and, furthermore, is a photometric precursor to the spectroscopic BOSS survey. Our galaxy catalogue and all power spectra data can be found at http://zuserver2.star.ucl.ac.uk/~sat/MegaZ/MegaZDR7.tar.gz.Comment: MNRAS Accepted: 20 pages - Galaxy catalogue and power spectra included onlin