HUBBLE SPACE TELESCOPE COMBINED STRONG AND WEAK LENSING ANALYSIS OF THE CLASH SAMPLE: MASS AND MAGNIFICATION MODELS AND SYSTEMATIC UNCERTAINTIES

By A. Zitrin, A. Fabris, Julian Merten, Peter Melchior, Massimo Meneghetti, A. Koekemoer, D. Coe, M. Maturi, M. Bartelmann, M. Postman and 16 more. 2014

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We present results from a comprehensive lensing analysis in Hubble Space Telescope (HST) data of the complete Cluster Lensing And Supernova survey with Hubble cluster sample. We identify previously undiscovered multiple images, allowing improved or first constraints on the cluster inner mass distributions and profiles. We combine these strong lensing constraints with weak lensing shape measurements within the HST field of view (FOV) to jointly constrain the mass distributions. The analysis is performed in two different common parameterizations (one adopts light-traces-mass for both galaxies and dark matter while the other adopts an analytical, elliptical Navarro–Frenk–White form for the dark matter) to provide a better assessment of the underlying systematics—which is most important for deep, cluster-lensing surveys, especially when studying magnified high-redshift objects. We find that the typical (median), relative systematic differences throughout the central FOV are ∼40% in the (dimensionless) mass density, κ, and ∼20% in the magnification, μ. We show maps of these differences for each cluster, as well as the mass distributions, critical curves, and two-dimensional (2D)-integrated mass profiles. For the Einstein radii (zs = 2) we find that all typically agree within 10% between the two models, and Einstein masses agree, typically, within ∼15%. At larger radii, the total projected, 2D-integrated mass profiles of the two models, within r ∼ 2′, differ by ∼30%. Stacking the surface-density profiles of the sample from the two methods together, we obtain an average slope of dlog (Σ)/dlog (r) ∼ −0.64 ± 0.1, in the radial range [5350] kpc. Last, we also characterize the behavior of the average magnification, surface density, and shear differences between the two models as a function of both the radius from the center and the best-fit values of these quantities. All mass models and magnification maps are made publicly available for the community.

Subfields: Astronomy and Astrophysics, Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/c76b2db46aed3171ead967fd6a1cd0971c393c5f

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Citations in the corpus

The z = 9-10 galaxy population in the Hubble Frontier Fields and CLASH surveys: the z = 9 luminosity function and further evidence for a smooth decline in ultraviolet luminosity density at z≥ 8 (2016, methodology): “For CLASH we also used mass models constructed by A. Zitrin et al. (2013, 2015), obtained through the Hubble Space Telescope Archive, as a high-end science product.”
The z ∼ 6 Luminosity Function Fainter than −15 mag from the Hubble Frontier Fields: The Impact of Magnification Uncertainties (2016, methodology): “…CATS, Sharon/Johnson, and Zitrin-NFW models, and the latter to include the Bradac et al. [2009], Grale [Liesenborgs et al. 2006; Sebesta et al. 2016], and Zitrin-LTM [Zitrin et al. 2012, 2015].10 A brief description of the general properties of the public lensing models can be found in Table 1.”
The Frontier Fields lens modelling comparison project (2016, methodology): “…procedure applies to all clusters - from relaxed, small clusters and groups (such as the relatively smaller cluster lenses A383, MS2137 or A611, see Zitrin et al. 2015, for recent modeling), to the most complex merging clusters such as M0416, M1149 or M0717 (Zitrin et al. 2015), that often require…”
CLASH: JOINT ANALYSIS OF STRONG-LENSING, WEAK-LENSING SHEAR, AND MAGNIFICATION DATA FOR 20 GALAXY CLUSTERS (2015, methodology): “Zitrin et al. (2015) recommended replacing the statistical errors of their models with the actual (and much larger) uncertainties that account for model-dependent systematics.”
The Frontier Fields: Survey Design and Initial Results (2016, background): “Additional programs have sought to understand and improve the systematics inherent in the different modeling approach (e.g. Zitrin et al. 2015; Mohammed et al. 2016; Harvey, Kneib, & Jauzac 2016; Meneghetti et al., in prep.).”