The Universal Einstein Radius Distribution from 10,000 SDSS Clusters
Adi Zitrin, Tom Broadhurst, Matthias Bartelmann, Yoel Rephaeli,, Masamune Oguri, Narciso Ben\'itez, Jiangang Hao, Keiichi Umetsu

TL;DR
This study analyzes 10,000 SDSS galaxy clusters to establish a universal distribution of Einstein radii, revealing a log-normal shape with more large-radius clusters than predicted by standard cosmological models.
Contribution
It introduces an automated, light-traces-mass method for estimating Einstein radii in large cluster samples without needing multiple images, validated with HST data.
Findings
Einstein radius distribution is log-normal with mean ~0.73 arcsec.
Higher abundance of large Einstein radius clusters than $\\Lambda$CDM predictions.
Approximately 20% of large-radius clusters are projection-boosted, with about 40 being genuine giant lenses.
Abstract
We present results from strong-lens modelling of 10,000 SDSS clusters, to establish the universal distribution of Einstein radii. Detailed lensing analyses have shown that the inner mass distribution of clusters can be accurately modelled by assuming light traces mass, successfully uncovering large numbers of multiple-images. Approximate critical curves and the effective Einstein radius of each cluster can therefore be readily calculated, from the distribution of member galaxies and scaled by their luminosities. We use a subsample of 10 well-studied clusters covered by both SDSS and HST to calibrate and test this method, and show that an accurate determination of the Einstein radius and mass can be achieved by this approach "blindly", in an automated way, and without requiring multiple images as input. We present the results of the first 10,000 clusters analysed in the range…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
