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 Article Swipe

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

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.5281/zenodo.4300381

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.

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Computer Science

Artificial Intelligence

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Markov chain Monte Carlo Inference Bayesian inference Bayesian probability Scale (ratio) Computer science Artificial intelligence Artificial neural network Hubble's law Constant (computer programming) Pattern recognition (psychology) Astrophysics Physics Galaxy Redshift Programming language Quantum mechanics

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Type: dataset
Language: en
Landing Page: https://doi.org/10.5281/zenodo.4300381
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DOI: https://doi.org/10.5281/zenodo.4300381

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Title: 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

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Language: en

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Publication year: 2020

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Publication date: 2020-12-01

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Authors: Ji Won Park, Sebastian Wagner-Carena, Simon Birrer, Philip J. Marshall, Joshua Yao-Yu Lin, A. Roodman

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Landing page: https://doi.org/10.5281/zenodo.4300381

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Concepts: Markov chain Monte Carlo, Inference, Bayesian inference, Bayesian probability, Scale (ratio), Computer science, Artificial intelligence, Artificial neural network, Hubble's law, Constant (computer programming), Pattern recognition (psychology), Astrophysics, Physics, Galaxy, Redshift, Programming language, Quantum mechanics

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