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Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing - Model Weights, Chains, BNN Samples, and Simulated Datasets

Name: Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing - Model Weights, Chains, BNN Samples, and Simulated Datasets
Keywords: Strong Gravitational Lensing, Hierarchical Inference, Dark Energy Science Collaboration, Rubin Observatory, Bayesian Neural Networks, Cosmology, Legacy Survey of Space and Time

Research datakeyboard_double_arrow_right Dataset 21 Oct 2020Publisher:Zenodo

Authors: Wagner-Carena, Sebastian; Park, Ji Won; Birrer, Simon; Marshall, Philip; Roodman, Aaron; Wechsler, Risa;

doi: 10.5281/zenodo.4116503 , 10.5281/zenodo.4116502

Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing - Model Weights, Chains, BNN Samples, and Simulated Datasets

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Abstract

The model weights, chains, simulated datasets, and BNN samples used to produce the results shown in LSST DESC Collaboration paper "Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing." All files presented here are meant for use in tandem with the python package "ovejero" (https://github.com/swagnercarena/ovejero).

Related Organizations

Stanford University
United States

Keywords

Strong Gravitational Lensing, Hierarchical Inference, Dark Energy Science Collaboration, Rubin Observatory, Bayesian Neural Networks, Cosmology, Legacy Survey of Space and Time

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