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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


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    <dct: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</dct:title>
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    <dcat:keyword>Cosmology</dcat:keyword>
    <dcat:keyword>Legacy Survey of Space and Time</dcat:keyword>
    <dcat:keyword>Rubin Observatory</dcat:keyword>
    <dcat:keyword>Bayesian Neural Network</dcat:keyword>
    <dcat:keyword>Dark Energy Science Collaboration</dcat:keyword>
    <dcat:keyword>Strong Gravitational Lensing</dcat:keyword>
    <dcat:keyword>Hierarchical Bayesian Inference</dcat:keyword>
    <dcat:keyword>Time Delay Cosmography</dcat:keyword>
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    <dct:description>&lt;p&gt;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, &amp;quot;Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant.&amp;quot; They are formatted to be used with the DESC package &amp;quot;H0rton&amp;quot; (&lt;a href="https://github.com/jiwoncpark/h0rton"&gt;https://github.com/jiwoncpark/h0rton&lt;/a&gt;). Additional descriptions can be found in the README. Please contact Ji Won Park (@jiwoncpark) on GitHub or &lt;a href="https://github.com/jiwoncpark/h0rton/issues"&gt;make an issue&lt;/a&gt; for any questions.&lt;/p&gt;</dct:description>
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