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Are Models of Strong Gravitational Lensing by Clusters Converging or Diverging?

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arxiv 2411.05083 v2 pith:I3BVCGRN submitted 2024-11-07 astro-ph.GA

classification astro-ph.GA
keywords modelsimageslensmassinputconvergingdistancenumber
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abstract

The increasingly large numbers of multiple images in cluster-scale gravitational lenses have allowed for tighter constraints on the mass distributions of these systems. Most lens models have progressed alongside this increase in image number. The general assumption is that these improvements would result in lens models converging to a common solution, suggesting that models are approaching the true mass distribution. To test whether or not this is occurring, we examine a sample of lens models of MACS J0416.1$-$2403 containing varying number of images as input. Splitting the sample into two bins (those including $<150$ and $>150$ images), we quantify the similarity of models in each bin using three comparison metrics, two of which are novel: Median Percent Difference, Frechet Distance, and Wasserstein Distance. In addition to quantifying similarity, the Frechet distance metric seems to also be an indicator of the mass sheet degeneracy. Each metric indicates that models with a greater number of input images are no more similar between one another than models with fewer input images. This suggests that lens models are neither converging nor diverging to a common solution for this system, regardless of method. With this result, we suggest that future models more carefully investigate lensing degeneracies and anomalous mass clumps (mass features significantly displaced from baryonic counterparts) to rigorously evaluate their model's validity. We also recommend further study into alternative, underutilized lens model priors (e.g. flux ratios) as an additional input constraint to image positions in hopes of breaking existing degeneracies.

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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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