What Multiple Images Say About the Large-Scale Mass Maps of Galaxy Clusters
Kekoa Lasko, Liliya L.R. Williams, Agniva Ghosh

TL;DR
This paper investigates how much information about galaxy cluster mass distributions can be derived solely from multiple lensed images, highlighting the relative influence of data versus model priors, especially on substructure scales.
Contribution
The study introduces a method to estimate global cluster properties from images alone, assessing the data's constraining power independent of priors across various simulated and real clusters.
Findings
Center, ellipticity, and position angle are well constrained by images.
Substructure correlation with true properties shows high scatter, indicating limited constraints.
Model priors dominate on small scales, affecting substructure reconstructions.
Abstract
All lens modeling methods, simply-parametrized, hybrid, and free-form, use assumptions to reconstruct galaxy clusters with multiply imaged sources, though the nature of these assumptions (priors) can differ considerably between methods. This raises an important question in strong lens modeling: how much information about the mass model comes from the lensed images themselves, and how much is a consequence of model priors. One way to assess the relative contributions of the lensing data vs. model priors is to estimate global lens properties through images alone, without any prior assumptions about the mass distribution. This is our approach. We use 200 mock cluster lenses, half of which have substructures which vary from clumpy and compact to smooth and extended; a simulated cluster Ares; and real clusters Abell 1689 and RXJ1347.5-1145 to show that the center, ellipticity, and position…
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Taxonomy
TopicsAstronomy and Astrophysical Research · Galaxies: Formation, Evolution, Phenomena · Adaptive optics and wavefront sensing
