Core Cosmology Library
Creators
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Chisari, Nora Elisa1
- Alonso, David2
- Krause, Elisabeth3
- Leonard, C. Danielle4
- Bull, Philip5
- Neveu, Jérémy6
- Villarreal, Antonio7
- Singh, Sukhdeep8
- McClintock, Thomas3
- Ellison, John9
- Du, Zilong9
- Zuntz, Joe10
- Mead, Alexander11
- Joudaki, Shahab2
- Lorenz, Christiane S.2
- Troster, Tilman10
- Sanchez, Javier12
- Lanusse, Francois4
- Ishak, Mustapha13
- Hlozek, Renée14
- Blazek, Jonathan15
- Campagne, Jean-Eric6
- Almoubayyed, Husni4
- Eifler, Tim3
- Kirby, Matthew3
- Kirkby, David12
- Plaszczynski, Stéphane6
- Slosar, Anze16
- Vrastil, Michal17
- Wagoner, Erika L.3
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Becker, Matthew R.7
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García Quintero, Cristhian13
-
Wang, Kuan18
- 1. Utrecht University
- 2. University of Oxford
- 3. University of Arizona
- 4. Carnegie Mellon University
- 5. Queen Mary University of London
- 6. Université Paris-Sud
- 7. Argonne National Laboratory
- 8. University of California, Berkeley
- 9. University of California, Riverside
- 10. Royal Observatory Edinburgh
- 11. University of British Columbia
- 12. University of California, Irvine
- 13. The University of Texas at Dallas
- 14. University of Toronto
- 15. Ohio State University
- 16. Brookhaven National Laboratory
- 17. Institute of Physics CAS
- 18. University of Pittsburgh
Description
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, halo profiles and the halo mass function through state-of-the-art modeling prescriptions available in the literature. 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.
Notes
Files
CCL-2.0.1.zip
Files
(73.9 MB)
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md5:089aea1cd1732a64a44f4b81ba35a240
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Additional details
Identifiers
Related works
- Is documented by
- https://arxiv.org/abs/1812.05995 (URL)