Galaxy clustering, photometric redshifts and diagnosis of systematics in the DES Science Verification data

We study the clustering of galaxies detected at $i<22.5$ in the Science Verification observations of the Dark Energy Survey (DES). Two-point correlation functions are measured using $2.3times 10^6$ galaxies over a contiguous 116 deg$^2$ region in five bins of photometric redshift width $Delta z = 0.2$ in the range $0.2 < z < 1.2.$ The impact of photometric redshift errors are assessed by comparing results using a template-based photo-$z$ algorithm (BPZ) to a machine-learning algorithm (TPZ). A companion paper (Leistedt et al 2015) presents maps of several observational variables (e.g. seeing, sky brightness) which could modulate the galaxy density. Here we characterize and mitigate systematic errors on the measured clustering which arise from these observational variables, in addition to others such as Galactic dust and stellar contamination. After correcting for systematic effects we measure galaxy bias over a broad range of linear scales relative to mass clustering predicted from the Planck $Lambda$CDM model, finding agreement with CFHTLS measurements with $chi^2$ of 4.0 (8.7) with 5 degrees of freedom for the TPZ (BPZ) redshifts. We test a linear bias model, in which the galaxy clustering is a fixed multiple of the predicted non-linear dark-matter clustering. The precision of the data allow us to determine that the linear bias model describes the observed galaxy clustering to $2.5%$ accuracy down to scales at least $4$ to $10$ times smaller than those on which linear theory is expected to be sufficient.

Simulating the Universe with MICE: The abundance of massive clusters

We introduce a new set of large N-body runs, the MICE simulations, that provide a unique combination of very large cosmological volumes with good mass resolution. They follow the gravitational evolution of ~ 8.5 billion particles (2048^3) in volumes covering up to 450 (Gpc/h)^3. Our main goal is to accurately model and calibrate basic cosmological probes that will be used by upcoming astronomical surveys. Here we take advantage of the very large volumes of MICE to make a robust sampling of the high-mass tail of the halo mass function (MF). We discuss and avoid possible systematic effects in our study, and do a detailed analysis of different error estimators. We find that available fits to the local abundance of halos (Warren et al. (2006)) match well the abundance in MICE up to M ~ 10^{14}Msun, but significantly deviate for larger masses, underestimating the mass function by 10% (30%) at M = 3.16 x 10^{14}Msun (10^{15}Msun). Similarly, the widely used Sheth & Tormen (1999) fit, if extrapolated to high redshift assuming universality, leads to an underestimation of the cluster abundance by 30%, 20% and 15% at z=0, 0.5, 1 for M ~ [7 - 2.5 - 0.8] x 10^{14}Msun respectively ($ u = delta_c/sigma ~ 3$). We provide a re-calibration of the halo MF valid over 5 orders of magnitude in mass, 10^{10} < M/(Msun) < 10^{15}, that accurately describes its redshift evolution up to z=1. We explore the impact of this re-calibration on the determination of dark-energy, and conclude that using available fits may systematically bias the estimate of w by as much as 50% for medium-depth (z <= 1) surveys. MICE halo catalogues are publicly available at http://www.ice.cat/mice