We examine the level of agreement between low-redshift weak lensing data and the cosmic microwave background using measurements from the Canada–France–Hawaii Telescope Lensing Survey (CFHTLenS) and ...Planck+Wilkinson Microwave Anisotropy Probe (WMAP) polarization. We perform an independent analysis of the CFHTLenS six bin tomography results of Heymans et al. We extend their systematics treatment and find the cosmological constraints to be relatively robust to the choice of non-linear modelling, extension to the intrinsic alignment model and inclusion of baryons. We find that when marginalized in the Ωm–σ8 plane, the 95 per cent confidence contours of CFHTLenS and Planck+WMAP only just touch, but the discrepancy is less significant in the full six-dimensional parameter space of Λ cold dark matter (ΛCDM). Allowing a massive active neutrino or tensor modes does not significantly resolve the tension in the full n-dimensional parameter space. Our results differ from some in the literature because we use the full tomographic information in the weak lensing data and marginalize over systematics. We note that adding a sterile neutrino to ΛCDM brings the 2D marginalized contours into greater overlap, mainly due to the extra effective number of neutrino species, which we find to be 0.88 ± 0.43 (68 per cent) greater than standard on combining the data sets. We discuss why this is not a completely satisfactory resolution, leaving open the possibility of other new physics or observational systematics as contributing factors. We provide updated cosmology fitting functions for the CFHTLenS constraints and discuss the differences from ones used in the literature.
The Core Cosmology Library (CCL) provides routines to compute basic cosmological observables to a high degree of accuracy, which have been verified with an extensive suite of validation tests. ...Predictions are provided for many cosmological quantities, including distances, angular power spectra, correlation functions, halo bias, and the halo mass function through state-of-the-art modeling prescriptions available in the literature. Fiducial specifications for the expected galaxy distributions for the Large Synoptic Survey Telescope (LSST) are also included, together with the capability of computing redshift distributions for a user-defined photometric redshift model. A rigorous validation procedure, based on comparisons between CCL and independent software packages, allows us to establish a well-defined numerical accuracy for each predicted quantity. As a result, predictions for correlation functions of galaxy clustering, galaxy-galaxy lensing, and cosmic shear are demonstrated to be within a fraction of the expected statistical uncertainty of the observables for the models and in the range of scales of interest to LSST. CCL is an open source software package written in C, with a Python interface and publicly available at https://github.com/LSSTDESC/CCL.
We construct forecasts for cosmological parameter constraints from weak gravitational lensing surveys involving the Square Kilometre Array (SKA). Considering matter content, dark energy and modified ...gravity parameters, we show that the first phase of the SKA (SKA1) can be competitive with other Stage III experiments such as the Dark Energy Survey and that the full SKA (SKA2) can potentially form tighter constraints than Stage IV optical weak lensing experiments, such as those that will be conducted with LSST, WFIRST-AFTA or Euclid-like facilities. Using weak lensing alone, going from SKA1 to SKA2 represents improvements by factors of ~10 in matter, ~10 in dark energy and ~5 in modified gravity parameters. We also show, for the first time, the powerful result that comparably tight constraints (within ~5 per cent) for both Stage III and Stage IV experiments, can be gained from cross-correlating shear maps between the optical and radio wavebands, a process which can also eliminate a number of potential sources of systematic errors which can otherwise limit the utility of weak lensing cosmology.
A unified analysis of four cosmic shear surveys Chang, Chihway; Wang, Michael; Dodelson, Scott ...
Monthly Notices of the Royal Astronomical Society,
01/2019, Letnik:
482, Številka:
3
Journal Article
Abstract
Large imaging surveys, such as the Legacy Survey of Space and Time, rely on photometric redshifts and tomographic binning for 3 × 2 pt analyses that combine galaxy clustering and weak ...lensing. In this paper, we propose a method for optimizing the tomographic binning choice for the lens sample of galaxies. We divide the CosmoDC2 and Buzzard simulated galaxy catalogs into a training set and an application set, where the training set is nonrepresentative in a realistic way, and then estimate photometric redshifts for the application sets. The galaxies are sorted into redshift bins covering equal intervals of redshift or comoving distance, or with an equal number of galaxies in each bin, and we consider a generalized extension of these approaches. We find that bins of equal comoving distance produce the highest dark energy figure of merit of the initial binning choices, but that the choice of bin edges can be further optimized. We then train a neural network classifier to identify galaxies that are either highly likely to have accurate photometric redshift estimates or highly likely to be sorted into the correct redshift bin. The neural network classifier is used to remove poor redshift estimates from the sample, and the results are compared to the case when none of the sample is removed. We find that the neural network classifiers are able to improve the figure of merit by ∼13% and are able to recover ∼25% of the loss in the figure of merit that occurs when a nonrepresentative training sample is used.
The GRavitational lEnsing Accuracy Testing 3 (GREAT3) challenge is the third in a series of image analysis challenges, with a goal of testing and facilitating the development of methods for analyzing ...astronomical images that will be used to measure weak gravitational lensing. This measurement requires extremely precise estimation of very small galaxy shape distortions, in the presence of far larger intrinsic galaxy shapes and distortions due to the blurring kernel caused by the atmosphere, telescope optics, and instrumental effects. Uie GREAT3 challenge is posed to the astronomy, machine learning, and statistics communities, and includes tests of three specific effects that are of immediate relevance to upcoming weak lensing surveys, two of which have never been tested in a community challenge before. These effects include many novel aspects including realistically complex galaxy models based on high-resolution imaging from space; a spatially varying, physically motivated blurring kernel; and a combination of multiple different exposures. To facilitate entry by people new to the field, and for use as a diagnostic tool, the simulation software for the challenge is publicly available, though the exact parameters used for the challenge are blinded. Sample scripts to analyze the challenge data using existing methods will also be provided. See http://great3challenge.info and http://great3.projects.phys.ucl.ac.uk/leaderboard/ for more information.
Using higher-order statistics to capture cosmological information from weak lensing surveys often requires a transformation of observed shear to a measurement of the convergence signal. This inverse ...problem is complicated by noise and boundary effects, and various reconstruction methods have been developed to implement the process. Here we evaluate the retention of signal information of four such methods: Kaiser-Squires, Wiener filter, , and . We use the higher order statistics to determine which method best reconstructs the original convergence with efficiency and precision. We find produces the tightest constraints on cosmological parameters, while Kaiser-Squires, Wiener filter, and are similar at a smoothing scale of 3.5 arcmin. We also study the MF inaccuracy caused by inappropriate training sets in the method and find it to be large compared to the errors, underlining the importance of selecting appropriate training cosmologies.
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