373 citations · 748 across the 9 of their papers we have counts for
9 papers
Lens Modeling of STRIDES Strongly Lensed Quasars using Neural Posterior Estimation
Sydney Erickson, Sebastian Wagner-Carena, Phil Marshall +9
Strongly lensed quasars can be used to constrain cosmological parameters through time-delay cosmography. Models of the lens masses are a necessary component of this analysis. To en…
A Strong Gravitational Lens Is Worth a Thousand Dark Matter Halos: Inference on Small-Scale Structure Using Sequential Methods
Sebastian Wagner-Carena, Jaehoon Lee, Jeffrey Pennington +3
Strong gravitational lenses are a singular probe of the universe's small-scale structure $\unicode{x2013}$ they are sensitive to the gravitational effects of low-mass $(<10^{10} M_…
Hierarchical Inference of the Lensing Convergence from Photometric Catalogs with Bayesian Graph Neural Networks
Ji Won Park, Simon Birrer, Madison Ueland +6
We present a Bayesian graph neural network (BGNN) that can estimate the weak lensing convergence () from photometric measurements of galaxies along a given line of sight. The me…
Symphony: Cosmological Zoom-in Simulation Suites over Four Decades of Host Halo Mass
Ethan O. Nadler, Philip Mansfield, Yunchong Wang +9
We present Symphony, a compilation of cosmological, cold-dark-matter-only zoom-in simulations spanning four decades of host halo mass, from $10^{15}~M_{\mathrm{\o…
From Images to Dark Matter: End-To-End Inference of Substructure From Hundreds of Strong Gravitational Lenses
Sebastian Wagner-Carena, Jelle Aalbers, Simon Birrer +4
Constraining the distribution of small-scale structure in our universe allows us to probe alternatives to the cold dark matter paradigm. Strong gravitational lensing offers a uniqu…
lenstronomy II: A gravitational lensing software ecosystem
Simon Birrer, Anowar J. Shajib, Daniel Gilman +18
lenstronomy is an Astropy-affiliated Python package for gravitational lensing simulations and analyses. lenstronomy was introduced by Birrer and Amara (2018) and is based on the li…
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…
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…
TDCOSMO IV: Hierarchical time-delay cosmography -- joint inference of the Hubble constant and galaxy density profiles
S. Birrer, A. J. Shajib, A. Galan +24
The H0LiCOW collaboration inferred via gravitational lensing time delays a Hubble constant km s, describing deflector mass density pro…