4.6 Article

Core Cosmology Library: Precision Cosmological Predictions for LSST

Journal

ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES
Volume 242, Issue 1, Pages -

Publisher

IOP PUBLISHING LTD
DOI: 10.3847/1538-4365/ab1658

Keywords

cosmology: theory; dark energy; large-scale structure of universe

Funding

  1. Centre National de la Recherche Scientifique
  2. National Energy Research Scientific Computing Center, a DOE Office of Science User Facility - Office of Science of the U.S. Department of Energy [DE-AC02-05CH11231]
  3. STFC DiRAC HPC Facilities - UK BIS National E-infrastructure capital grants
  4. UK particle physics grid - GridPP Collaboration
  5. DOE [DE-AC02-76SF00515]
  6. Science and Technology Facilities Council (STFC) through an Ernest Rutherford Fellowship [ST/P004474/1]
  7. Beecroft fellowship
  8. Royal Astronomical Society Research Fellowship
  9. European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant [797794]
  10. NSF [AST-1517768]
  11. U.S. Department of Energy, Office of Science [DE-SC0019206]

Ask authors/readers for more resources

The Core Cosmology Library (CCL) provides routines to compute basic cosmological observables to a high degree of accuracy, which have been verified with an extensive suite of validation tests. Predictions are provided for many cosmological quantities, including distances, angular power spectra, correlation functions, halo bias, and the halo mass function through state-of-the-art modeling prescriptions available in the literature. Fiducial specifications for the expected galaxy distributions for the Large Synoptic Survey Telescope (LSST) are also included, together with the capability of computing redshift distributions for a user-defined photometric redshift model. A rigorous validation procedure, based on comparisons between CCL and independent software packages, allows us to establish a well-defined numerical accuracy for each predicted quantity. As a result, predictions for correlation functions of galaxy clustering, galaxy-galaxy lensing, and cosmic shear are demonstrated to be within a fraction of the expected statistical uncertainty of the observables for the models and in the range of scales of interest to LSST. CCL is an open source software package written in C, with a Python interface and publicly available at. https://github.com/LSSTDESC/CCL.

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