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

Cosmological baryonic and matter densities from 600,000 SDSS Luminous Red Galaxies with photometric redshifts

We analyze MegaZ-LRG, a photometric-redshift catalogue of Luminous Red Galaxies (LRGs) based on the imaging data of the Sloan Digital Sky Survey (SDSS) 4th Data Release. MegaZ-LRG, presented in a companion paper, contains 10^6 photometric redshifts derived with ANNz, an Artificial Neural Network method, constrained by a spectroscopic sub-sample of 13,000 galaxies obtained by the 2dF-SDSS LRG and Quasar (2SLAQ) survey. The catalogue spans the redshift range 0.4 < z < 0.7 with an r.m.s. redshift error ~ 0.03(1+z), covering 5,914 deg^2 to map out a total cosmic volume 2.5 h^-3 Gpc^3. In this study we use the most reliable 600,000 photometric redshifts to present the first cosmological parameter fits to galaxy angular power spectra from a photometric redshift survey. Combining the redshift slices with appropriate covariances, we determine best-fitting values for the matter and baryon densities of Omega_m h = 0.195 +/- 0.023 and Omega_b/Omega_m = 0.16 +/- 0.036 (with the Hubble parameter h = 0.75 and scalar index of primordial fluctuations n = 1 held fixed). These results are in agreement with and independent of the latest studies of the Cosmic Microwave Background radiation, and their precision is comparable to analyses of contemporary spectroscopic-redshift surveys. We perform an extensive series of tests which conclude that our power spectrum measurements are robust against potential systematic photometric errors in the catalogue. We conclude that photometric-redshift surveys are competitive with spectroscopic surveys for measuring cosmological parameters in the simplest vanilla models. Future deep imaging surveys have great potential for further improvement, provided that systematic errors can be controlled.Comment: 24 pages, 23 figures, MNRAS accepte

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

Mock galaxy redshift catalogues from simulations: implications for Pan-STARRS1

We describe a method for constructing mock galaxy catalogues which are well suited for use in conjunction with large photometric surveys. We use the semi-analytic galaxy formation model of Bower et al. implemented in the Millennium simulation. We apply our method to the specific case of the surveys soon to commence with PS1, the first of 4 telescopes planned for the Pan-STARRS system. PS1 has 5 photometric bands (grizy), and will carry out an all-sky 3pi survey and a medium deep survey (MDS) over 84 sq.deg. We calculate the expected magnitude limits for extended sources in the two surveys. We find that, after 3 years, the 3pi survey will have detected over 10^8 galaxies in all 5 bands, 10 million of which will lie at redshift z>0.9, while the MDS will have detected over 10^7 galaxies with 0.5 million lying at z>2. These numbers at least double if detection in the shallowest band, y is not required. We then evaluate the accuracy of photometric redshifts estimated using an off-the-shelf photo-z code. With the grizy bands alone it is possible to achieve an accuracy in the 3pi survey of Delta z/(1+z)~0.06 for 0.25<z<0.8, which could be reduced by about 15% using near infrared photometry from the UKIDDS survey, but would increase by about 25% for the deeper sample without the y band photometry. For the MDS an accuracy of Delta z/(1+z)~0.05 is achievable for 0.02<z<1.5 using grizy. A dramatic improvement in accuracy is possible by selecting only red galaxies. In this case, Delta z/(1+z)~0.02-0.04 is achievable for ~100 million galaxies at 0.4<z<1.1 in the 3pi survey and for 30 million galaxies in the MDS at 0.4<z<2. We investigate the effect of using photo-z in the estimate of the baryonic acoustic oscillation scale. We find that PS1 will achieve a similar accuracy in this estimate as a spectroscopic survey of 20 million galaxies.Comment: 23 pages, 18 figures, accepted by MNRA

Learning few-shot imitation as cultural transmission

Abstract Cultural transmission is the domain-general social skill that allows agents to acquire and use information from each other in real-time with high fidelity and recall. It can be thought of as the process that perpetuates fit variants in cultural evolution. In humans, cultural evolution has led to the accumulation and refinement of skills, tools and knowledge across generations. We provide a method for generating cultural transmission in artificially intelligent agents, in the form of few-shot imitation. Our agents succeed at real-time imitation of a human in novel contexts without using any pre-collected human data. We identify a surprisingly simple set of ingredients sufficient for generating cultural transmission and develop an evaluation methodology for rigorously assessing it. This paves the way for cultural evolution to play an algorithmic role in the development of artificial general intelligence