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We publish the training/validation/test datasets, trained model weights, configuration files, Bayesian neural network samples, and MCMC chains used to produce the figures in the LSST DESC paper, "Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant." They are formatted to be used with the DESC package "H0rton" (https://github.com/jiwoncpark/h0rton). Additional descriptions can be found in the README. Please contact Ji Won Park (@jiwoncpark) on GitHub or make an issue for any questions.
Bayesian Neural Network, Strong Gravitational Lensing, Dark Energy Science Collaboration, Time Delay Cosmography, Rubin Observatory, Hierarchical Bayesian Inference, Cosmology, Legacy Survey of Space and Time
Bayesian Neural Network, Strong Gravitational Lensing, Dark Energy Science Collaboration, Time Delay Cosmography, Rubin Observatory, Hierarchical Bayesian Inference, Cosmology, Legacy Survey of Space and Time
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