February 5, 2020 Software Open Access

Martin Millon; Vivien Bonvin

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{
  "description": "<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>\nhttps://github.com/shsuyu/H0LiCOW-public/tree/master/H0_inference_code<br>\n<br>\nThe posterior distributions of the time-delay distances and angular diameter distances for five of the six lens systems can be downloaded here:<br>\nhttps://github.com/shsuyu/H0LiCOW-public/tree/master/h0licow_distance_chains<br>\nThe remaining lens (B1608+656) has an analytical fit to the PDF.</p>\n\n<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>\n\n<ul>\n\t<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>\n\t<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>\n\t<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>\n\t<li><a href=\"https://ui.adsabs.harvard.edu/abs/2019MNRAS.484.4726B/abstract\">Birrer et al. 2019</a> (J1206+4332 distance posterior)</li>\n\t<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>\n\t<li><a href=\"https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.1743C/abstract\">Chen et al. 2019</a> (PG1115+080 distance posterior)</li>\n\t<li><a href=\"https://arxiv.org/abs/1905.09338\">Rusu et al. 2019</a> (WFI2033-4723 distance posterior)</li>\n\t<li><a href=\"https://arxiv.org/abs/1907.04869\">Wong et al. 2019</a> (combined inference)</li>\n</ul>\n\n<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>\nhttps://github.com/shsuyu/H0LiCOW-public/tree/master/cosmo_parameter_chains</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne", 
      "@id": "https://orcid.org/0000-0001-7051-497X", 
      "@type": "Person", 
      "name": "Martin Millon"
    }, 
    {
      "affiliation": "Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne", 
      "@id": "https://orcid.org/0000-0003-1471-3952", 
      "@type": "Person", 
      "name": "Vivien Bonvin"
    }
  ], 
  "url": "https://zenodo.org/record/3633035", 
  "datePublished": "2020-02-05", 
  "version": "v1.0", 
  "keywords": [
    "H0LiCOW", 
    "Cosmology", 
    "Hubble constant"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.3633035", 
  "@id": "https://doi.org/10.5281/zenodo.3633035", 
  "@type": "SoftwareSourceCode", 
  "name": "H0LiCOW cosmological parameter sampling software"
}