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Aaron Roodman

for the LSST Dark Energy Science Collaboration

Hierarchical Inference of the Lensing Convergence from Photometric Catalogs with Bayesian Graph Neural Networks


Nov 15, 2022
Ji Won Park, Simon Birrer, Madison Ueland, Miles Cranmer, Adriano Agnello, Sebastian Wagner-Carena, Philip J. Marshall, Aaron Roodman, the LSST Dark Energy Science Collaboration

* 15 pages, 8 figures (+ 6 pages, 2 figures in Appendix). Submitted to ApJ. Code at https://github.com/jiwoncpark/node-to-joy 

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Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes


Jun 18, 2021
Ji Won Park, Ashley Villar, Yin Li, Yan-Fei Jiang, Shirley Ho, Joshua Yao-Yu Lin, Philip J. Marshall, Aaron Roodman

* 6 pages, 4 figures, 1 table, written for non-astronomers, submitted to the ICML 2021 Time Series and Uncertainty and Robustness in Deep Learning Workshops. Comments welcome! Added affiliations and references for Fig 1 

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Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant


Nov 30, 2020
Ji Won Park, Sebastian Wagner-Carena, Simon Birrer, Philip J. Marshall, Joshua Yao-Yu Lin, Aaron Roodman

* 21 pages (+2 appendix), 17 figures. To be submitted to ApJ. Code at https://github.com/jiwoncpark/h0rton 

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Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing


Oct 28, 2020
Sebastian Wagner-Carena, Ji Won Park, Simon Birrer, Philip J. Marshall, Aaron Roodman, Risa H. Wechsler

* Code available at https://github.com/swagnercarena/ovejero 

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