CosmoLike is a collaborative software development project to analyze cosmological data sets and to forecast future missions.
We are deeply involved in the ongoing analysis of the Dark Energy Survey, and in optimizing the Large Synoptic Survey Telescope and the Wide-Field Infrared Survey Telescope. Members of our team are also working on projects related to the Hyper Suprime Cam Survey, the Euclid mission, and various CMB experiments.
Most importantly however, we want to support small, innovative theory projects that test new concepts and methods to enhance the constraining power of cosmological analyses.
The code base is organized as connected repositories on GitHub. If you have a project in mind, please contact us.
Completed projects and papers
Krause & Eifler 2017 CosmoLike - Cosmological Likelihood Analyses for Photometric Galaxy Surveys, MNRAS, Volume 470, Issue 2, p.2100-2112
We explore strategies to extract cosmological constraints from a joint analysis of cosmic shear, galaxy-galaxy lensing, galaxy clustering, cluster number counts and cluster weak lensing. We utilize the cosmolike software to simulate results from a Large Synoptic Survey Telescope (LSST) like data set, specifically, we (1) compare individual and joint analyses of the different probes, (2) vary the selection criteria for lens and source galaxies, (3) investigate the impact of blending, (4) investigate the impact of the assumed cosmological model in multiprobe covariances, (6) quantify information content as a function of scales and (7) explore the impact of intrinsic galaxy alignment in a multiprobe context. Our analyses account for all cross-correlations within and across probes and include the higher-order (non-Gaussian) terms in the multiprobe covariance matrix. We simultaneously model cosmological parameters and a variety of systematics, e.g. uncertainties arising from shear and photo-z calibration, cluster mass-observable relation, galaxy intrinsic alignment and galaxy bias (up to 54 parameters altogether). We highlight two results: first, increasing the number density of source galaxies by ∼30 per cent, which corresponds to solving blending for LSST, only gains little information. Secondly, including small scales in clustering and galaxy-galaxy lensing, by utilizing halo occupation distribution models, can substantially boost cosmological constraining power.