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arxiv: 1601.05779 · v1 · submitted 2016-01-21 · 🌌 astro-ph.CO

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CosmoLike - Cosmological Likelihood Analyses for Photometric Galaxy Surveys

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classification 🌌 astro-ph.CO
keywords cosmologicalgalaxycosmolikeanalysesclusterimpactlensingmulti-probe
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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 an 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 multi-probe covariances, 6) quantify information content as a function of scales, and 7) explore the impact of intrinsic galaxy alignment in a multi-probe context. Our analyses account for all cross correlations within and across probes and include the higher-order (non-Gaussian) terms in the multi-probe 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%, which corresponds to solving blending for LSST, only gains little information. Second, including small scales in clustering and galaxy-galaxy lensing, by utilizing HODs, can substantially boost cosmological constraining power. The CosmoLike modules used to compute the results in this paper will be made publicly available at https://github.com/elikrause/CosmoLike_Forecasts.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. UNIONS-3500 Weak Lensing: III. 2D Cosmological Constraints in Configuration Space

    astro-ph.CO 2026-05 accept novelty 5.0

    UNIONS-3500 weak lensing data yields S_8 = 0.831^{+0.067}_{-0.078} in flat LCDM from 2D cosmic shear, consistent with Planck within 1 sigma.

  2. Machine-learning applications for weak-lensing cosmology

    astro-ph.CO 2026-05 unverdicted novelty 2.0

    Machine learning techniques can mitigate limitations in traditional weak-lensing analyses and enhance extraction of cosmological information from galaxy imaging surveys.