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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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  <identifier identifierType="DOI">10.5281/zenodo.4300382</identifier>
  <creators>
    <creator>
      <creatorName>Park, Ji Won</creatorName>
      <givenName>Ji Won</givenName>
      <familyName>Park</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-0692-1092</nameIdentifier>
      <affiliation>Stanford University / SLAC</affiliation>
    </creator>
    <creator>
      <creatorName>Wagner-Carena, Sebastian</creatorName>
      <givenName>Sebastian</givenName>
      <familyName>Wagner-Carena</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-5039-1685</nameIdentifier>
      <affiliation>Stanford University / SLAC</affiliation>
    </creator>
    <creator>
      <creatorName>Birrer, Simon</creatorName>
      <givenName>Simon</givenName>
      <familyName>Birrer</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-3195-5507</nameIdentifier>
      <affiliation>Stanford University</affiliation>
    </creator>
    <creator>
      <creatorName>Marshall, Philip J.</creatorName>
      <givenName>Philip J.</givenName>
      <familyName>Marshall</familyName>
      <affiliation>Stanford University / SLAC</affiliation>
    </creator>
    <creator>
      <creatorName>Lin, Joshua Yao-Yu</creatorName>
      <givenName>Joshua Yao-Yu</givenName>
      <familyName>Lin</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-0680-4838</nameIdentifier>
      <affiliation>University of Illinois at Urbana-Champaign</affiliation>
    </creator>
    <creator>
      <creatorName>Roodman, Aaron</creatorName>
      <givenName>Aaron</givenName>
      <familyName>Roodman</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-5326-3486</nameIdentifier>
      <affiliation>Stanford University / SLAC</affiliation>
    </creator>
  </creators>
  <titles>
    <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</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>Cosmology</subject>
    <subject>Legacy Survey of Space and Time</subject>
    <subject>Rubin Observatory</subject>
    <subject>Bayesian Neural Network</subject>
    <subject>Dark Energy Science Collaboration</subject>
    <subject>Strong Gravitational Lensing</subject>
    <subject>Hierarchical Bayesian Inference</subject>
    <subject>Time Delay Cosmography</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-12-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4300382</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.4300381</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/lsst-desc</relatedIdentifier>
  </relatedIdentifiers>
  <version>v1.0</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&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;</description>
  </descriptions>
</resource>
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