Software Open Access
<?xml version='1.0' encoding='utf-8'?> <resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd"> <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"><p>Python notebook for H<sub>0</sub> inference using H0LiCOW collaboration&#39;s 6-lens distance measurements.&nbsp; The python notebook is also available here:<br> https://github.com/shsuyu/H0LiCOW-public/tree/master/H0_inference_code<br> <br> The posterior distributions of the time-delay distances and angular diameter distances for five of the six lens systems can be downloaded here:<br> https://github.com/shsuyu/H0LiCOW-public/tree/master/h0licow_distance_chains<br> The remaining lens (B1608+656) has an analytical fit to the PDF.</p> <p>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:</p> <ul> <li><a href="https://ui.adsabs.harvard.edu/abs/2010ApJ...711..201S/abstract">Suyu et al. 2010</a> (B1608+656 time-delay distance fit)</li> <li><a href="https://ui.adsabs.harvard.edu/abs/2019Sci...365.1134J/abstract">Jee et al. 2019</a> (B1608+656 angular diameter distance fit)</li> <li><a href="https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.1743C/abstract">Chen et al. 2019</a>, <a href="https://ui.adsabs.harvard.edu/abs/2017MNRAS.465.4895W/abstract">Wong et al. 2017</a> (HE0435-1223 distance posterior)</li> <li><a href="https://ui.adsabs.harvard.edu/abs/2019MNRAS.484.4726B/abstract">Birrer et al. 2019</a> (J1206+4332 distance posterior)</li> <li><a href="https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.1743C/abstract">Chen et al. 2019</a>, <a href="https://ui.adsabs.harvard.edu/abs/2014ApJ...788L..35S/abstract">Suyu et al. 2014</a> (RXJ1131-1231 distance posterior)</li> <li><a href="https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.1743C/abstract">Chen et al. 2019</a> (PG1115+080 distance posterior)</li> <li><a href="https://arxiv.org/abs/1905.09338">Rusu et al. 2019</a> (WFI2033-4723 distance posterior)</li> <li><a href="https://arxiv.org/abs/1907.04869">Wong et al. 2019</a> (combined inference)</li> </ul> <p>The H<sub>0</sub> inference from these posteriors can be obtained following the python notebook.&nbsp; The cosmological parameter chains from running the python notebook are available here:<br> https://github.com/shsuyu/H0LiCOW-public/tree/master/cosmo_parameter_chains</p></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> </fundingReferences> </resource>
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