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CosmicPy performs simple and interactive cosmology computations for forecasting cosmological parameters constraints; it computes tomographic and 3D Spherical Fourier-Bessel power spectra as well as Fisher matrices for galaxy clustering. Written in Python, it relies on a fast C++ implementation of Fourier-Bessel related computations, and requires NumPy, SciPy, and Matplotlib.
The DESCQA framework provides rigorous validation protocols for assessing the quality of high-quality simulated sky catalogs in a straightforward and comprehensive way. DESCQA enables the inspection, validation, and comparison of an inhomogeneous set of synthetic catalogs via the provision of a common interface within an automated framework. An interactive web interface is also available at https://portal.nersc.gov/projecta/lsst/descqa/v2/.
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.
DeepMass infers dark matter maps from weak gravitational lensing measurements and uses deep learning to reconstruct cosmological maps. The code can also be incorporated into a Moment Network to enable high-dimensional likelihood-free inference.
BornRaytrace uses neural data compression of weak lensing map summary statistics to simulate weak gravitational lensing effects. It can raytrace through overdensity Healpix maps to return a convergence map, include shear-kappa transformation on the full sphere, and also include intrinsic alignments (NLA model).