1605.05333
Time delay cosmography
Treu, Marshall
Gravitational time delays, observed in strong lens systems where the variable background sources is multiply-imaged by a massive galaxy in the foreground, provide direct measurements of cosmo distance that are very complementary to other cosmographic probes. The success of the technique depends on the availability and size of a suitable sample of lensed quasars or supernovae, precise measurements of the time delays, accurate modeling of the gravitational potential of the main deflector, and the ability to characterize the distribution of mass along the LoS to the source. Review the progress made during the last 15 years, doing which the first competitive cosmological inferences with time delays were made, and look ahead to the potential of significantly larger lens samples in the near future.
1605.05337
Measurement of a cosmographic distance ratio with galaxy and CMB lensing
Miyatake, et al
Measure the GL shear signal around DM haloes hosting CMASS galaxies using light sources at z~1 (background galaxies) and at the surface of last scattering at z~1100 (the CMB). The galaxy shear measurement uses data from the CFHTLenS survey, and the microwave background shear measurement uses data from the Planck satellite. The ratio of shears from these cross-correlations provides a purely geometric distance measurement across the longest possible cosmological lever arm. This is because the matter distribution around the haloes, including uncertainties in galaxy bias and systematic errors such as miscentering, cancels in the ratio exactly. Measure this distance ratio in 3 different z slices of the CMASS sample, and combine them to obtain a 15% measurement of the distance ratio, r=0.344±0.052 at an effective redshift of z=0.54. This is consistent with the predicted ratio from the Planck best-fit LCDM cosmology of r=0.410.
1605.05501
Clustering-based redshift estimation: application to VIPERS/CHFTLS
Scooter, Mellier, et al
Explore the accuracy of the clustering-based redshift estimation proposed by Menard+2013 when applied to VIPERS and CFHTLS real data. This method enables reconstruction z distributions from measurement of the angular clustering of objects using a set of secure spectroscopic redshifts. Use state of the art spectroscopic measurements with iAB<22.5 from VIPERS as reference population to infer the z distribution of galaxies from the CFHTLS T0007 release. VIPERS provides a nearly representative sample to the fox limit iAB<22.5 at z>0.5 which allows to test the accuracy of the clustering-based redshift distributions. Show that this method enables reproduction of the true mean color-z relation when both populations have the same magnitude limit. Also show that this technique allows the inference of z distributions for a population fainter than the one of reference and give an estimate of the color-z mapping in this case. This last point is of great interest for future large z surveys which suffer from the need of a complete faint spectroscopic sample.
1505.05700
Cluster-lensing: a python package for galaxy clusters & miscentering
Ford, VanderPlas
Describe a new open scoured package for calculating properties of galaxy clusters, including NFW halo profiles with and without the effects of cluster miscentering. This pure-Python package, cluster-lensing, provides well-documented and easy-to-use classes and functions for calculating cluster scaling relations, including mass-richness and mass-concentration relations from the literature, as well as the surface mass density Sigma(R) and different surface mass density DeltaSigma(R) profiles, probed by WL magnification and shear. Galaxy cluster miscentering is especially a concern for stacked WL shear studies of galaxy clusters, where offsets between the assumed and the true underlying matter distribution can lead to a significant bias in the mass estimates if not accounted for. This software has been developed and released in GitHub, and is licensed under the permissive MIT license. The cluster-leninsg package is archived on Zenodo (Ford 2016). Full documentation, source code, and installation instructions are available at http://jesford.github.io/cluster-lensing/.
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