February 5, 2020 Software Open Access

Martin Millon; Vivien Bonvin

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  <identifier identifierType="DOI">10.5281/zenodo.3633035</identifier>
  <creators>
    <creator>
      <creatorName>Martin Millon</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-7051-497X</nameIdentifier>
      <affiliation>Ecole Polytechnique Fédérale de Lausanne</affiliation>
    </creator>
    <creator>
      <creatorName>Vivien Bonvin</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-1471-3952</nameIdentifier>
      <affiliation>Ecole Polytechnique Fédérale de Lausanne</affiliation>
    </creator>
  </creators>
  <titles>
    <title>H0LiCOW cosmological parameter sampling software</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>H0LiCOW</subject>
    <subject>Cosmology</subject>
    <subject>Hubble constant</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-02-05</date>
  </dates>
  <resourceType resourceTypeGeneral="Software"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3633035</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsDerivedFrom" resourceTypeGeneral="Software">https://github.com/shsuyu/H0LiCOW-public/tree/master/H0_inference_code</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3633034</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;Python notebook for H&lt;sub&gt;0&lt;/sub&gt; inference using H0LiCOW collaboration&amp;#39;s 6-lens distance measurements.&amp;nbsp; The python notebook is also available here:&lt;br&gt;
https://github.com/shsuyu/H0LiCOW-public/tree/master/H0_inference_code&lt;br&gt;
&lt;br&gt;
The posterior distributions of the time-delay distances and angular diameter distances for five of the six lens systems can be downloaded here:&lt;br&gt;
https://github.com/shsuyu/H0LiCOW-public/tree/master/h0licow_distance_chains&lt;br&gt;
The remaining lens (B1608+656) has an analytical fit to the PDF.&lt;/p&gt;

&lt;p&gt;If you make use of the distance measurements (time-delay distance and/or lens angular diameter distance) to the 6 lens systems from H0LiCOW, please cite the relevant publications:&lt;/p&gt;

&lt;ul&gt;
	&lt;li&gt;&lt;a href="https://ui.adsabs.harvard.edu/abs/2010ApJ...711..201S/abstract"&gt;Suyu et al. 2010&lt;/a&gt; (B1608+656 time-delay distance fit)&lt;/li&gt;
	&lt;li&gt;&lt;a href="https://ui.adsabs.harvard.edu/abs/2019Sci...365.1134J/abstract"&gt;Jee et al. 2019&lt;/a&gt; (B1608+656 angular diameter distance fit)&lt;/li&gt;
	&lt;li&gt;&lt;a href="https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.1743C/abstract"&gt;Chen et al. 2019&lt;/a&gt;, &lt;a href="https://ui.adsabs.harvard.edu/abs/2017MNRAS.465.4895W/abstract"&gt;Wong et al. 2017&lt;/a&gt; (HE0435-1223 distance posterior)&lt;/li&gt;
	&lt;li&gt;&lt;a href="https://ui.adsabs.harvard.edu/abs/2019MNRAS.484.4726B/abstract"&gt;Birrer et al. 2019&lt;/a&gt; (J1206+4332 distance posterior)&lt;/li&gt;
	&lt;li&gt;&lt;a href="https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.1743C/abstract"&gt;Chen et al. 2019&lt;/a&gt;, &lt;a href="https://ui.adsabs.harvard.edu/abs/2014ApJ...788L..35S/abstract"&gt;Suyu et al. 2014&lt;/a&gt; (RXJ1131-1231 distance posterior)&lt;/li&gt;
	&lt;li&gt;&lt;a href="https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.1743C/abstract"&gt;Chen et al. 2019&lt;/a&gt; (PG1115+080 distance posterior)&lt;/li&gt;
	&lt;li&gt;&lt;a href="https://arxiv.org/abs/1905.09338"&gt;Rusu et al. 2019&lt;/a&gt; (WFI2033-4723 distance posterior)&lt;/li&gt;
	&lt;li&gt;&lt;a href="https://arxiv.org/abs/1907.04869"&gt;Wong et al. 2019&lt;/a&gt; (combined inference)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;The H&lt;sub&gt;0&lt;/sub&gt; inference from these posteriors can be obtained following the python notebook.&amp;nbsp; The cosmological parameter chains from running the python notebook are available here:&lt;br&gt;
https://github.com/shsuyu/H0LiCOW-public/tree/master/cosmo_parameter_chains&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100001711</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/SNSF/Project+funding/200020_172712/">200020_172712</awardNumber>
      <awardTitle>COSMOGRAIL: Cosmology with Time Delays in Gravitationally Lensed Quasars</awardTitle>
    </fundingReference>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/787886/">787886</awardNumber>
      <awardTitle>Cosmology with Strong Gravitational Lensing</awardTitle>
    </fundingReference>
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