191 citations · 226 across the 5 of their papers we have counts for
7 papers
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…
Multi-segment preserving sampling for deep manifold sampler
Daniel Berenberg, Jae Hyeon Lee, Simon Kelow +6
Deep generative modeling for biological sequences presents a unique challenge in reconciling the bias-variance trade-off between explicit biological insight and model flexibility.…
Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes
Ji Won Park, Ashley Villar, Yin Li +5
Among the most extreme objects in the Universe, active galactic nuclei (AGN) are luminous centers of galaxies where a black hole feeds on surrounding matter. The variability patter…
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…