191 citations · 384 across the 10 of their papers we have counts for
10 papers
Latent Stochastic Differential Equations for Modeling Quasar Variability and Inferring Black Hole Properties
Joshua Fagin, Ji Won Park, Henry Best +6
Quasars are bright and unobscured active galactic nuclei (AGN) thought to be powered by the accretion of matter around supermassive black holes at the centers of galaxies. The temp…
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…
DESC DC2 Data Release Note
LSST Dark Energy Science Collaboration, Bela Abolfathi, Robert Armstrong +54
In preparation for cosmological analyses of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), the LSST Dark Energy Science Collaboration (LSST DESC) has created…
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…
The LSST DESC DC2 Simulated Sky Survey
LSST Dark Energy Science Collaboration, Bela Abolfathi, David Alonso +77
We describe the simulated sky survey underlying the second data challenge (DC2) carried out in preparation for analysis of the Vera C. Rubin Observatory Legacy Survey of Space and…
Proceedings of the first MadAnalysis 5 workshop on LHC recasting in Korea
Benjamin Fuks, Samuel Bein, Guillaume Chalons +22
We present the activities performed during the first MadAnalysis 5 workshop on LHC recasting that has been organized at High 1 (Gangwon privince, Korea) on August 20-27, 2017. This…