299 citations · 1.5k across the 86 of their papers we have counts for
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Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant
Ji Won Park, Sebastian Wagner-Carena, Simon Birrer +3
We investigate the use of approximate Bayesian neural networks (BNNs) in modeling hundreds of time-delay gravitational lenses for Hubble constant () determination. Our BNN was…
The evolution of the size-mass relation at =1-3 derived from the complete Hubble Frontier Fields data set
Lilan Yang, Guido Roberts-Borsani, Tommaso Treu +3
We measure the size-mass relation and its evolution between redshifts 13, using galaxies lensed by six foreground Hubble Frontier Fields clusters. The power afforded by strong…
High-resolution imaging follow-up of doubly imaged quasars
Anowar J. Shajib, Eden Molina, Adriano Agnello +8
We report upon three years of follow-up and confirmation of doubly imaged quasar lenses through imaging campaigns from 2016-2018 with the Near-Infrared Camera2 (NIRC2) on the W. M.…
The impact of mass map truncation on strong lensing simulations
Lyne Van de Vyvere, Dominique Sluse, Sampath Mukherjee +2
Strong gravitational lensing is a powerful tool to measure cosmological parameters and to study galaxy evolution mechanisms. However, quantitative strong lensing studies often requ…
Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing
Sebastian Wagner-Carena, Ji Won Park, Simon Birrer +3
In the past few years, approximate Bayesian Neural Networks (BNNs) have demonstrated the ability to produce statistically consistent posteriors on a wide range of inference problem…
Combining strong and weak lensing estimates in the Cosmos field
Felix Arjun Kuhn, Claudio Bruderer, Simon Birrer +2
We present a combined cosmic shear analysis of the modeling of line-of-sight distortions on strongly lensed extended arcs and galaxy shape measurements in the COSMOS field. We deve…