[en] Assessing the consistency of parameter constraints derived from different cosmological probes is an important way to test the validity of the underlying cosmological model. In an earlier work [1], we computed constraints on cosmological parameters for ΛCDM from an integrated analysis of CMB temperature anisotropies and CMB lensing from Planck, galaxy clustering and weak lensing from SDSS, weak lensing from DES SV as well as Type Ia supernovae and Hubble parameter measurements. In this work, we extend this analysis and quantify the concordance between the derived constraints and those derived by the Planck Collaboration as well as WMAP9, SPT and ACT. As a measure for consistency, we use the Surprise statistic [2], which is based on the relative entropy. In the framework of a flat ΛCDM cosmological model, we find all data sets to be consistent with one another at a level of less than 1σ. We highlight that the relative entropy is sensitive to inconsistencies in the models that are used in different parts of the analysis. In particular, inconsistent assumptions for the neutrino mass break its invariance on the parameter choice. When consistent model assumptions are used, the data sets considered in this work all agree with each other and ΛCDM, without evidence for tensions.

[en] With the high-precision data from current and upcoming experiments, it becomes increasingly important to perform consistency tests of the standard cosmological model. In this work, we focus on consistency measures between different data sets and methods that allow us to assess the goodness of fit of different models. We address both of these questions using the relative entropy or Kullback-Leibler (KL) divergence [1]. First, we revisit the relative entropy as a consistency measure between data sets and further investigate some of its key properties, such as asymmetry and path dependence. We then introduce a novel model rejection framework, which is based on the relative entropy and the posterior predictive distribution. We validate the method on several toy models and apply it to Type Ia supernovae data from the JLA and CMB constraints from Planck 2015, testing the consistency of the data with six different cosmological models.

[en] Image simulations are becoming increasingly important in understanding the measurement process of the shapes of galaxies for weak lensing and the associated systematic effects. For this purpose we present the first implementation of the Monte Carlo Control Loops (MCCL), a coherent framework for studying systematic effects in weak lensing. It allows us to model and calibrate the shear measurement process using image simulations from the Ultra Fast Image Generator (UFig) and the image analysis software SExtractor. We apply this framework to a subset of the data taken during the Science Verification period (SV) of the Dark Energy Survey (DES). We calibrate the UFig simulations to be statistically consistent with one of the SV images, which covers ∼0.5 square degrees. We then perform tolerance analyses by perturbing six simulation parameters and study their impact on the shear measurement at the one-point level. This allows us to determine the relative importance of different parameters. For spatially constant systematic errors and point-spread function, the calibration of the simulation reaches the weak lensing precision needed for the DES SV survey area. Furthermore, we find a sensitivity of the shear measurement to the intrinsic ellipticity distribution, and an interplay between the magnitude-size and the pixel value diagnostics in constraining the noise model. This work is the first application of the MCCL framework to data and shows how it can be used to methodically study the impact of systematics on the cosmic shear measurement

[en] We construct a simple phenomenological model for the evolving galaxy population by incorporating predefined baryonic prescriptions into a dark matter hierarchical merger tree. The model is based on the simple gas-regulator model introduced by Lilly et al., coupled with the empirical quenching rules of Peng et al. The simplest model already does quite well in reproducing, without re-adjusting the input parameters, many observables, including the main sequence sSFR-mass relation, the faint end slope of the galaxy mass function, and the shape of the star forming and passive mass functions. Similar to observations and/or the recent phenomenological model of Behroozi et al., which was based on epoch-dependent abundance-matching, our model also qualitatively reproduces the evolution of the main sequence sSFR(z) and SFRD(z) star formation rate density relations, the M_s – M_h stellar-to-halo mass relation, and the SFR – M_h relation. Quantitatively the evolution of sSFR(z) and SFRD(z) is not steep enough, the M_s – M_h relation is not quite peaked enough, and, surprisingly, the ratio of quenched to star forming galaxies around M* is not quite high enough. We show that these deficiencies can simultaneously be solved by ad hoc allowing galaxies to re-ingest some of the gas previously expelled in winds, provided that this is done in a mass-dependent and epoch-dependent way. These allow the model galaxies to reduce an inherent tendency to saturate their star formation efficiency, which emphasizes how efficient galaxies around M* are in converting baryons into stars and highlights the fact that quenching occurs at the point when galaxies are rapidly approaching the maximum possible efficiency of converting baryons into stars.

[en] We study the substructure content of the strong gravitational lens RXJ1131-1231 through a forward modelling approach that relies on generating an extensive suite of realistic simulations. We use a semi-analytic merger tree prescription that allows us to stochastically generate substructure populations whose properties depend on the dark matter particle mass. These synthetic halos are then used as lenses to produce realistic mock images that have the same features, e.g. luminous arcs, quasar positions, instrumental noise and PSF, as the data. We then analyse the data and the simulations in the same way with summary statistics that are sensitive to the signal being targeted and are able to constrain models of dark matter statistically using Approximate Bayesian Computing (ABC) techniques. (In this work, we focus on the thermal relic mass estimate and fix the semi-analytic descriptions of the substructure evolution based on recent literature.) We are able, based on the HST data for RXJ1131-1231, to rule out a warm dark matter thermal relic mass below 2 keV at the 2σ confidence level.

[en] We present a strong lensing modeling technique based on versatile basis sets for the lens and source planes. Our method uses high performance Monte Carlo algorithms, allows for an adaptive build up of complexity, and bridges the gap between parametric and pixel based reconstruction methods. We apply our method to a Hubble Space Telescope image of the strong lens system RX J1131-1231 and show that our method finds a reliable solution and is able to detect substructure in the lens and source planes simultaneously. Using mock data, we show that our method is sensitive to sub-clumps with masses four orders of magnitude smaller than the main lens, which corresponds to about ${10}^8{M}_{\odot <!--\; ???\; -->},$ without prior knowledge of the position and mass of the sub-clump. The modeling approach is flexible and maximizes automation to facilitate the analysis of the large number of strong lensing systems expected in upcoming wide field surveys. The resulting search for dark sub-clumps in these systems, without mass-to-light priors, offers promise for probing physics beyond the standard model in the dark matter sector.

[en] We present extended modelling of the strong lens system RXJ1131-1231 with archival data in two HST bands in combination with existing line-of-sight contribution and velocity dispersion estimates. Our focus is on source size and its influence on time-delay cosmography. We therefore examine the impact of mass-sheet degeneracy and especially the degeneracy pointed out by Schneider and Sluse (2013) [1] using the source reconstruction scale. We also extend on previous work by further exploring the effects of priors on the kinematics of the lens and the external convergence in the environment of the lensing system. Our results coming from RXJ1131-1231 are given in a simple analytic form so that they can be easily combined with constraints coming from other cosmological probes. We find that the choice of priors on lens model parameters and source size are subdominant for the statistical errors for H ₀ measurements of this systems. The choice of prior for the source is sub-dominant at present (2% uncertainty on H ₀) but may be relevant for future studies. More importantly, we find that the priors on the kinematic anisotropy of the lens galaxy have a significant impact on our cosmological inference. When incorporating all the above modeling uncertainties, we find H ₀ = 86.6^+6.8_-6.9 km s^-1 Mpc^-1, when using kinematic priors similar to other studies. When we use a different kinematic prior motivated by Barnabè et al. (2012) [2] but covering the same anisotropic range, we find H ₀ = 74.5^+8.0_-7.8 km s^-1 Mpc^-1. This means that the choice of kinematic modeling and priors have a significant impact on cosmographic inferences. The way forward is either to get better velocity dispersion measures which would down weight the impact of the priors or to construct physically motivated priors for the velocity dispersion model.

[en] We present the results of a study to optimize the principal component analysis (PCA) algorithm for planet detection, a new algorithm complementing angular differential imaging and locally optimized combination of images (LOCI) for increasing the contrast achievable next to a bright star. The stellar point spread function (PSF) is constructed by removing linear combinations of principal components, allowing the flux from an extrasolar planet to shine through. The number of principal components used determines how well the stellar PSF is globally modeled. Using more principal components may decrease the number of speckles in the final image, but also increases the background noise. We apply PCA to Fomalhaut Very Large Telescope NaCo images acquired at 4.05 μm with an apodized phase plate. We do not detect any companions, with a model dependent upper mass limit of 13-18 M _Jup from 4-10 AU. PCA achieves greater sensitivity than the LOCI algorithm for the Fomalhaut coronagraphic data by up to 1 mag. We make several adaptations to the PCA code and determine which of these prove the most effective at maximizing the signal-to-noise from a planet very close to its parent star. We demonstrate that optimizing the number of principal components used in PCA proves most effective for pulling out a planet signal.

[en] We present a simple method to accurately infer line of sight (LOS) integrated lensing effects for galaxy scale strong lens systems through image reconstruction. Our approach enables us to separate weak lensing LOS effects from the main strong lens deflector. We test our method using mock data and show that strong lens systems can be accurate probes of cosmic shear with a precision on the shear terms of ± 0.003 (statistical error) for an HST-like dataset. We apply our formalism to reconstruct the lens COSMOS 0038+4133 and its LOS. In addition, we estimate the LOS properties with a halo-rendering estimate based on the COSMOS field galaxies and a galaxy-halo connection. The two approaches are independent and complementary in their information content. We find that when estimating the convergence at the strong lens system, performing a joint analysis improves the measure by a factor of two compared to a halo model only analysis. Furthermore the constraints of the strong lens reconstruction lead to tighter constraints on the halo masses of the LOS galaxies. Joint constraints of multiple strong lens systems may add valuable information to the galaxy-halo connection and may allow independent weak lensing shear measurement calibrations.

[en] Bayesian inference is often used in cosmology and astrophysics to derive constraints on model parameters from observations. This approach relies on the ability to compute the likelihood of the data given a choice of model parameters. In many practical situations, the likelihood function may however be unavailable or intractable due to non-gaussian errors, non-linear measurements processes, or complex data formats such as catalogs and maps. In these cases, the simulation of mock data sets can often be made through forward modeling. We discuss how Approximate Bayesian Computation (ABC) can be used in these cases to derive an approximation to the posterior constraints using simulated data sets. This technique relies on the sampling of the parameter set, a distance metric to quantify the difference between the observation and the simulations and summary statistics to compress the information in the data. We first review the principles of ABC and discuss its implementation using a Population Monte-Carlo (PMC) algorithm and the Mahalanobis distance metric. We test the performance of the implementation using a Gaussian toy model. We then apply the ABC technique to the practical case of the calibration of image simulations for wide field cosmological surveys. We find that the ABC analysis is able to provide reliable parameter constraints for this problem and is therefore a promising technique for other applications in cosmology and astrophysics. Our implementation of the ABC PMC method is made available via a public code release