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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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{
  "description": "<p>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, &quot;Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant.&quot; They are formatted to be used with the DESC package &quot;H0rton&quot; (<a href=\"https://github.com/jiwoncpark/h0rton\">https://github.com/jiwoncpark/h0rton</a>). Additional descriptions can be found in the README. Please contact Ji Won Park (@jiwoncpark) on GitHub or <a href=\"https://github.com/jiwoncpark/h0rton/issues\">make an issue</a> for any questions.</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Stanford University / SLAC", 
      "@id": "https://orcid.org/0000-0002-0692-1092", 
      "@type": "Person", 
      "name": "Park, Ji Won"
    }, 
    {
      "affiliation": "Stanford University / SLAC", 
      "@id": "https://orcid.org/0000-0001-5039-1685", 
      "@type": "Person", 
      "name": "Wagner-Carena, Sebastian"
    }, 
    {
      "affiliation": "Stanford University", 
      "@id": "https://orcid.org/0000-0003-3195-5507", 
      "@type": "Person", 
      "name": "Birrer, Simon"
    }, 
    {
      "affiliation": "Stanford University / SLAC", 
      "@type": "Person", 
      "name": "Marshall, Philip J."
    }, 
    {
      "affiliation": "University of Illinois at Urbana-Champaign", 
      "@id": "https://orcid.org/0000-0003-0680-4838", 
      "@type": "Person", 
      "name": "Lin, Joshua Yao-Yu"
    }, 
    {
      "affiliation": "Stanford University / SLAC", 
      "@id": "https://orcid.org/0000-0001-5326-3486", 
      "@type": "Person", 
      "name": "Roodman, Aaron"
    }
  ], 
  "url": "https://zenodo.org/record/4300382", 
  "datePublished": "2020-12-01", 
  "version": "v1.0", 
  "keywords": [
    "Cosmology", 
    "Legacy Survey of Space and Time", 
    "Rubin Observatory", 
    "Bayesian Neural Network", 
    "Dark Energy Science Collaboration", 
    "Strong Gravitational Lensing", 
    "Hierarchical Bayesian Inference", 
    "Time Delay Cosmography"
  ], 
  "@context": "https://schema.org/", 
  "distribution": [
    {
      "contentUrl": "https://zenodo.org/api/files/40dcc442-30b3-42a4-ba0a-62c3dbd9d3b2/data_generation_config.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/40dcc442-30b3-42a4-ba0a-62c3dbd9d3b2/test_v7.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/40dcc442-30b3-42a4-ba0a-62c3dbd9d3b2/trained_models.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/40dcc442-30b3-42a4-ba0a-62c3dbd9d3b2/train_v7.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/40dcc442-30b3-42a4-ba0a-62c3dbd9d3b2/val_v7.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/40dcc442-30b3-42a4-ba0a-62c3dbd9d3b2/inference_results.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }
  ], 
  "identifier": "https://doi.org/10.5281/zenodo.4300382", 
  "@id": "https://doi.org/10.5281/zenodo.4300382", 
  "@type": "Dataset", 
  "name": "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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