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Research software . Software . 2020

H0LiCOW cosmological parameter sampling software

Millon, Martin; Bonvin, Vivien;
Open Access
Published: 05 Feb 2020
Publisher: Zenodo
Abstract

Python notebook for H0 inference using H0LiCOW collaboration's 6-lens distance measurements. The python notebook is also available here: https://github.com/shsuyu/H0LiCOW-public/tree/master/H0_inference_code The posterior distributions of the time-delay distances and angular diameter distances for five of the six lens systems can be downloaded here: https://github.com/shsuyu/H0LiCOW-public/tree/master/h0licow_distance_chains The remaining lens (B1608+656) has an analytical fit to the PDF. 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: Suyu et al. 2010 (B1608+656 time-delay distance fit) Jee et al. 2019 (B1608+656 angular diameter distance fit) Chen et al. 2019, Wong et al. 2017 (HE0435-1223 distance posterior) Birrer et al. 2019 (J1206+4332 distance posterior) Chen et al. 2019, Suyu et al. 2014 (RXJ1131-1231 distance posterior) Chen et al. 2019 (PG1115+080 distance posterior) Rusu et al. 2019 (WFI2033-4723 distance posterior) Wong et al. 2019 (combined inference) The H0 inference from these posteriors can be obtained following the python notebook. The cosmological parameter chains from running the python notebook are available here: https://github.com/shsuyu/H0LiCOW-public/tree/master/cosmo_parameter_chains

Subjects

H0LiCOW, Cosmology, Hubble constant

Funded by
EC| COSMICLENS
Project
COSMICLENS
Cosmology with Strong Gravitational Lensing
  • Funder: European Commission (EC)
  • Project Code: 787886
  • Funding stream: H2020 | ERC | ERC-ADG
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