Cosmology

Abstract:

Context. The quest for the cosmological parameters that describe our universe continues to motivate the scientific community to undertake very large survey initiatives across the electromagnetic spectrum. Over the past two decades, the Chandra and XMM-Newton observatories have supported numerous studies of X-ray-selected clusters of galaxies, active galactic nuclei (AGNs), and the X-ray background. The present paper is the first in a series reporting results of the XXL-XMM survey; it comes at a time when the Planck mission results are being finalised.

Aims. We present the XXL Survey, the largest XMM programme totaling some 6.9 Ms to date and involving an international consortium of roughly 100 members. The XXL Survey covers two extragalactic areas of 25 deg² each at a point-source sensitivity of ~5 × 10^-15 erg s^-1 cm^-2 in the [0.5−2] keV band (completeness limit). The survey’s main goals are to provide constraints on the dark energy equation of state from the space-time distribution of clusters of galaxies and to serve as a pathfinder for future, wide-area X-ray missions. We review science objectives, including cluster studies, AGN evolution, and large-scale structure, that are being conducted with the support of approximately 30 follow-up programmes.

Methods. We describe the 542 XMM observations along with the associated multi-λ and numerical simulation programmes. We give a detailed account of the X-ray processing steps and describe innovative tools being developed for the cosmological analysis.

Results. The paper provides a thorough evaluation of the X-ray data, including quality controls, photon statistics, exposure and background maps, and sky coverage. Source catalogue construction and multi-λ associations are briefly described. This material will be the basis for the calculation of the cluster and AGN selection functions, critical elements of the cosmological and science analyses.

Conclusions. The XXL multi-λ data set will have a unique lasting legacy value for cosmological and extragalactic studies and will serve as a calibration resource for future dark energy studies with clusters and other X-ray selected sources. With the present article, we release the XMM XXL photon and smoothed images along with the corresponding exposure maps.

Abstract:

Intrinsic galaxy shape and angular momentum alignments can arise in cosmological large-scale structure due to tidal interactions or galaxy formation processes. Cosmological hydrodynamical simulations have recently come of age as a tool to study these alignments and their contamination to weak gravitational lensing. We probe the redshift and luminosity evolution of intrinsic alignments in Horizon-AGN between z=0 and z=3 for galaxies with an r-band absolute magnitude of <-20. Alignments transition from being radial at low redshifts and high luminosities, dominated by the contribution of ellipticals, to being tangential at high redshift and low luminosities, where discs dominate the signal. This cannot be explained by the evolution of the fraction of ellipticals and discs alone: intrinsic evolution in the amplitude of alignments is necessary. The alignment amplitude of elliptical galaxies alone is smaller in amplitude by a factor of ~2, but has similar luminosity and redshift evolution as in current observations and in the nonlinear tidal alignment model at projected separations of > 1 Mpc. Alignments of discs are null in projection and consistent with current low redshift observations. The combination of the two populations yields an overall amplitude a factor of ~4 lower than observed alignments of luminous red galaxies with a steeper luminosity dependence. The restriction on accurate galaxy shapes implies that the galaxy population in the simulation is complete only to an r-band absolute magnitude of <-20. Higher resolution simulations will be necessary to avoid extrapolation of the intrinsic alignment predictions to the range of luminosities probed by future surveys.

Abstract:

The next generation of cosmology experiments will be required to use photometric redshifts rather than spectroscopic redshifts. Obtaining accurate and well-characterized photometric redshift distributions is therefore critical for Euclid, the Large Synoptic Survey Telescope and the Square Kilometre Array. However, determining accurate variance predictions alongside single point estimates is crucial, as they can be used to optimize the sample of galaxies for the specific experiment (e.g. weak lensing, baryon acoustic oscillations, supernovae), trading off between completeness and reliability in the galaxy sample. The various sources of uncertainty in measurements of the photometry and redshifts put a lower bound on the accuracy that any model can hope to achieve. The intrinsic uncertainty associated with estimates is often non-uniform and input-dependent, commonly known in statistics as heteroscedastic noise. However, existing approaches are susceptible to outliers and do not take into account variance induced by non-uniform data density and in most cases require manual tuning of many parameters. In this paper, we present a Bayesian machine learning approach that jointly optimizes the model with respect to both the predictive mean and variance we refer to as Gaussian processes for photometric redshifts (GPz). The predictive variance of the model takes into account both the variance due to data density and photometric noise. Using the SDSS DR12 data, we show that our approach substantially outperforms other machine learning methods for photo-z estimation and their associated variance, such as tpz and annz2. We provide a matlab and python implementations that are available to download at https://github.com/OxfordML/GPz.