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[ascl:1807.012]
AngPow: Fast computation of accurate tomographic power spectra

AngPow computes the auto (z1 = z2) and cross (z1 ≠ z2) angular power spectra between redshift bins (i.e. Cℓ(z1,z2)). The developed algorithm is based on developments on the Chebyshev polynomial basis and on the Clenshaw-Curtis quadrature method. AngPow is flexible and can handle any user-defined power spectra, transfer functions, bias functions, and redshift selection windows. The code is fast enough to be embedded inside programs exploring large cosmological parameter spaces through the Cℓ(z1,z2) comparison with data.

[ascl:1901.003]
CCL: Core Cosmology Library

Chisari, Nora Elisa; Alonso, David; Krause, Elisabeth; Leonard, C. Daniellle; Bull, Philip; Neveu, Jérémy; Villarreal, Antonio; Singh, Sukhdeep; McClintock, Thomas; Ellison, John; Du, Zilong; Zuntz, Joe; Mead, Alexander; Joudaki, Shahab; Lorenz, Christiane S.; Troester, Tilman; Sanchez, Javier; Lanusse, Francois; Ishak, Mustapha; Hlozek, Renée; Blazek, Jonathan; Campagne, Jean-Eric; Almoubayyed, Husni; Eifler, Tim; Kirby, Matthew; Kirkby, David; Plaszczynski, Stéphane; Slosar, Anze; Vrastil, Michal; Wagoner, Erika L.

The Core Cosmology Library (CCL) computes basic cosmological observables and provides predictions 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. 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. Predictions for correlation functions of galaxy clustering, galaxy-galaxy lensing and cosmic shear are 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 written in C and has a python interface.

[submitted]
Sacc: Save All Correlations and Covariances

Zuntz, J.; Slosar, A.; Alonso, D.; Becker, M.; Broussard, A.; McClintock, T.; Nicola, A.; Miyatake, H.; Sanchez, J.; Neveu, J.

SACC (Save All Correlations and Covariances) is a format and reference library for general storage

of summary statistic measurements for the Dark Energy Science Collaboration (DESC) within and from the Large Synoptic Survey Telescope (LSST) project's Dark Energy Science Collaboration.