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Free-form Grale reconstruction of Abell 2744: robustness of uncertainties against changes in lensing data

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arxiv 1907.09492 v1 pith:WGCHMO3O submitted 2019-07-22 astro-ph.CO

classification astro-ph.CO
keywords datagalaxieslensingabellmasschangesclustergrale
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Abell 2744, a massive Hubble Frontier Fields merging galaxy cluster with many multiple images in the core has been the subject of many lens inversions using different methods. While most existing studies compare various inversion methods, we focus on a comparison of reconstructions that use different input lensing data. Since the quantity and quality of lensing data is constantly improving, it makes sense to ask if the estimated uncertainties are robust against changes in the data. We address this question using free-form Grale, which takes only image information as input, and nothing pertaining to cluster galaxies. We reconstruct Abell 2744 using two sets of strong lensing data from the Hubble Frontier Fields community. Our first and second reconstructions use 55 and 91 images, respectively, and only 10 of the 91 images have the same positions and redshifts as in the first reconstruction. Comparison of the two mass maps shows that Grale uncertainties are robust against these changes, as well as small modifications in the inversion routine. Additionally, applying the methods used in Sebesta et al. (2016) for MACS J0416, we conclude that, in a statistical sense, light follows mass in Abell 2744, with brighter galaxies clustering stronger with the recovered mass than the fainter ones. We also show that the faintest galaxies are anti-correlated with mass, which is likely the result of light contamination from bright galaxies, and lensing magnification bias acting on galaxies background to the cluster.

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  1. The Jackknife method as a new approach to validate strong lens mass models

    astro-ph.CO 2025-05 conditional novelty 5.0 of 10

    A leave-one-source-out Jackknife cross-validation distinguishes accurate from overfitted strong lens mass models in cases where the reduced chi-square cannot.

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