Dataset Open Access

Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant - Datasets, Trained Models, BNN Samples, and MCMC Chains

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

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.

Files (29.0 GB)
Name Size
data_generation_config.zip
md5:d1c9572664c8150f61e0e9b850ec2895
186 Bytes Download
inference_results.zip
md5:c0ed7162ed00837b5c05ee7d1fa32936
8.5 GB Download
test_v7.zip
md5:0ec4d935b9fc26d20cefefed5357e937
16.3 MB Download
train_v7.zip
md5:0f0c526185df77d1e9124055e9f6a7e7
16.3 GB Download
trained_models.zip
md5:4836fac49fdb68293e5693cf342fc6a1
4.1 GB Download
val_v7.zip
md5:c101840f4c6cc432c931482e9fb0a634
16.3 MB Download
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