Filter-substrate refraction causes dominant lateral shifts yielding 0.3-0.4% PSF size and ellipticity residuals across most Roman bands that exceed weak lensing requirements by an order of magnitude, while longitudinal defocus shifts remain negligible.
CosmoLike - Cosmological Likelihood Analyses for Photometric Galaxy Surveys
9 Pith papers cite this work. Polarity classification is still indexing.
9
Pith papers citing it
abstract
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.
citation-role summary
background 2
method 1
citation-polarity summary
representative citing papers
COLA-based hybrid emulator reproduces nonlinear power spectrum boosts in w0wa models to <2% error vs EuclidEmulator2 and produces <0.3σ shifts in LSST-like cosmic shear parameter constraints.
DES Y3 3x2pt analysis constrains S8=0.776±0.017 and Ωm=0.339±0.032 in flat ΛCDM, consistent with Planck CMB results at p=0.13-0.48.
Fixing the covariance at an incorrect cosmology in cluster count analyses leaves Ω_c, σ_8, and w estimates unbiased but distorts their uncertainties, driven by S_8 amplitude effects; a single update at the recovered best-fit cosmology restores correct normalization for LSST-like surveys.
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.
Simulated likelihood analysis shows Limber approximation, neglected RSD, and approximate nonlinear power spectra each induce cosmological biases of ~1 sigma or more (exceeding 2 sigma for Rubin) in Roman and Rubin 3x2pt studies.
Using CMB, SN, BAO and shear data, the work shows dynamical dark energy in MG models correlates with deviations from GR below z=2 at >95% CL, a link that holds for varying sound speed but vanishes for a cosmological constant.
The improved Narrow Kernel Approximation fails to match simulation-based covariances for real-space cosmic shear 2PCF under survey masks due to off-diagonal harmonic-space errors, while the weighted quartic-counts method agrees better.
Machine learning techniques can mitigate limitations in traditional weak-lensing analyses and enhance extraction of cosmological information from galaxy imaging surveys.
citing papers explorer
-
Modeling the impact of filter-substrate refraction in the Roman point spread function
Filter-substrate refraction causes dominant lateral shifts yielding 0.3-0.4% PSF size and ellipticity residuals across most Roman bands that exceed weak lensing requirements by an order of magnitude, while longitudinal defocus shifts remain negligible.
-
Modeling nonlinear scales for dynamical dark energy cosmologies with COLA
COLA-based hybrid emulator reproduces nonlinear power spectrum boosts in w0wa models to <2% error vs EuclidEmulator2 and produces <0.3σ shifts in LSST-like cosmic shear parameter constraints.
-
Dark Energy Survey Year 3 Results: Cosmological Constraints from Galaxy Clustering and Weak Lensing
DES Y3 3x2pt analysis constrains S8=0.776±0.017 and Ωm=0.339±0.032 in flat ΛCDM, consistent with Planck CMB results at p=0.13-0.48.
-
Cosmology-dependent covariance in galaxy cluster number counts: consequences for parameter inference
Fixing the covariance at an incorrect cosmology in cluster count analyses leaves Ω_c, σ_8, and w estimates unbiased but distorts their uncertainties, driven by S_8 amplitude effects; a single update at the recovered best-fit cosmology restores correct normalization for LSST-like surveys.
-
UNIONS-3500 Weak Lensing: III. 2D Cosmological Constraints in Configuration Space
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.
-
Accurate modeling for 3$\times$2pt analyses in Roman and Rubin: a study of model approximations
Simulated likelihood analysis shows Limber approximation, neglected RSD, and approximate nonlinear power spectra each induce cosmological biases of ~1 sigma or more (exceeding 2 sigma for Rubin) in Roman and Rubin 3x2pt studies.
-
The sound of dynamical dark energy and modified gravity
Using CMB, SN, BAO and shear data, the work shows dynamical dark energy in MG models correlates with deviations from GR below z=2 at >95% CL, a link that holds for varying sound speed but vanishes for a cosmological constant.
-
Analytical method for computing the covariance matrix of cosmic shear two-point correlation function
The improved Narrow Kernel Approximation fails to match simulation-based covariances for real-space cosmic shear 2PCF under survey masks due to off-diagonal harmonic-space errors, while the weighted quartic-counts method agrees better.
-
Machine-learning applications for weak-lensing cosmology
Machine learning techniques can mitigate limitations in traditional weak-lensing analyses and enhance extraction of cosmological information from galaxy imaging surveys.