32 citations · 86 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
deeplenstronomy: A dataset simulation package for strong gravitational lensing
Robert Morgan, Brian Nord, Simon Birrer +2
Automated searches for strong gravitational lensing in optical imaging survey datasets often employ machine learning and deep learning approaches. These techniques require more exa…
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…
Anomaly Detection for Multivariate Time Series of Exotic Supernovae
V. Ashley Villar, Miles Cranmer, Gabriella Contardo +2
Supernovae mark the explosive deaths of stars and enrich the cosmos with heavy elements. Future telescopes will discover thousands of new supernovae nightly, creating a need to fla…
Deblending and Classifying Astronomical Sources with Mask R-CNN Deep Learning
Colin J. Burke, Patrick D. Aleo, Yu-Ching Chen +4
We apply a new deep learning technique to detect, classify, and deblend sources in multi-band astronomical images. We train and evaluate the performance of an artificial neural net…