scholarly journals Joint analysis of cluster number counts and weak lensing power spectrum to correct for the super-sample covariance

2014 ◽  
Vol 441 (3) ◽  
pp. 2456-2475 ◽  
Author(s):  
M. Takada ◽  
D. N. Spergel
Keyword(s):  
Power Spectrum ◽  
Joint Analysis ◽  
Weak Lensing ◽  
Cluster Number ◽  
Sample Covariance ◽  
Number Counts

Cosmic Data Fusion

2005 ◽  
Vol 201 ◽  
pp. 368-376
Author(s):  
S. L. Bridle
Keyword(s):  
Power Spectrum ◽  
Large Scale ◽  
Cosmological Parameters ◽  
Big Bang ◽  
Joint Analysis ◽  
Data Sets ◽  
Cluster Number ◽  
Scale Invariant ◽  
Peculiar Velocities ◽  
Initial Power

We compare and combine likelihood functions of the cosmological parameters Ωm, h and σ8 from the CMB, type Ia supernovae and from probes of large scale structure. We include the recent results from the CMB experiments BOOMERANG and MAXIMA-1. Our analysis assumes a flat ACDM cosmology with a scale-invariant adiabatic initial power spectrum. First we consider three data sets that directly probe the mass in the Universe, without the need to relate the galaxy distribution to the underlying mass via a “biasing” relation: peculiar velocities, CMB and supernovae. We assume a baryonic fraction as inferred from Big-Bang Nucleosynthesis and find that all three data sets agree well, overlapping significantly at the 2σ level. This therefore justifies a joint analysis, in which we find a joint best fit point and 95% confidence limits of Ωm = 0.28 (0.17, 0.39), h = 0.74 (0.64, 0.86), and σ8 = 1.17 (0.98,1.37). Secondly we extend our earlier work on combining CMB, supernovae, cluster number counts, IRAS galaxy redshift survey data to include BOOMERANG and MAXIMA-1 data and to allow a free Ωbh2. We find that, given our assumption of a scale invariant initial power spectrum (n = 1), we obtain the robust result of Ωbh2 = 0.031 ± 0.03, which is dominated by the CMB constraint.

scholarly journals Constraints from thermal Sunyaev-Zel’dovich cluster counts and power spectrum combined with CMB

2018 ◽  
Vol 614 ◽  
pp. A13 ◽  
Author(s):  
Laura Salvati ◽  
Marian Douspis ◽  
Nabila Aghanim
Keyword(s):  
Dark Energy ◽  
Equation Of State ◽  
Power Spectrum ◽  
Large Scale ◽  
Cosmological Parameters ◽  
State Parameter ◽  
Cluster Number ◽  
Number Count ◽  
First Case ◽  
Number Counts

The thermal Sunyaev-Zel’dovich (tSZ) effect is one of the recent probes of cosmology and large-scale structures. We update constraints on cosmological parameters from galaxy clusters observed by the Planck satellite in a first attempt to combine cluster number counts and the power spectrum of hot gas; we used a new value of the optical depth and, at the same time, sampling on cosmological and scaling-relation parameters. We find that in the ΛCDM model, the addition of a tSZ power spectrum provides small improvements with respect to number counts alone, leading to the 68% c.l. constraints Ωm = 0.32  ± 0.02, σ8 = 0.76  ± 0.03, and σ8(Ωm/0.3)1/3 = 0.78  ± 0.03 and lowering the discrepancy with results for cosmic microwave background (CMB) primary anisotropies (updated with the new value of τ) to ≃1.8σ on σ8. We analysed extensions to the standard model, considering the effect of massive neutrinos and varying the equation of state parameter for dark energy. In the first case, we find that the addition of the tSZ power spectrum helps in improving cosmological constraints with respect to number count alone results, leading to the 95% upper limit ∑ mν < 1.88 eV. For the varying dark energy equation of state scenario, we find no important improvements when adding tSZ power spectrum, but still the combination of tSZ probes is able to provide constraints, producing w = −1.0 ± 0.2. In all cosmological scenarios, the mass bias to reconcile CMB and tSZ probes remains low at (1 − b) ≲ 0.67 as compared to estimates from weak lensing and X-ray mass estimate comparisons or numerical simulations.

scholarly journals Calibration of bias and scatter involved in cluster mass measurements using optical weak gravitational lensing

2021 ◽  
Author(s):  
Sebastian Grandis ◽  
Sebastian Bocquet ◽  
Joseph J Mohr ◽  
Matthias Klein ◽  
Klaus Dolag
Keyword(s):  
Gravitational Lensing ◽  
Initial Conditions ◽  
Stage Iv ◽  
Weak Lensing ◽  
Mass Relation ◽  
Cluster Number ◽  
Mass Calibration ◽  
Hydrodynamical Simulations ◽  
Number Counts ◽  
The Impact

Abstract Cosmological inference from cluster number counts is systematically limited by the accuracy of the mass calibration, i.e. the empirical determination of the mapping between cluster selection observables and halo mass. In this work we demonstrate a method to quantitatively determine the bias and uncertainties in weak-lensing mass calibration. To this end, we extract a library of projected matter density profiles from hydrodynamical simulations. Accounting for shear bias and noise, photometric redshift uncertainties, mis-centering, cluster member contamination, cluster morphological diversity, and line-of-sight projections, we produce a library of shear profiles. Fitting a one-parameter model to these profiles, we extract the so-called weak lensing mass MWL. Relating the weak-lensing mass to the halo mass from gravity-only simulations with the same initial conditions as the hydrodynamical simulations allows us to estimate the impact of hydrodynamical effects on cluster number counts experiments. Creating new shear libraries for ∼1000 different realizations of the systematics, provides a distribution of the parameters of the weak-lensing to halo mass relation, reflecting their systematic uncertainty. This result can be used as a prior for cosmological inference. We also discuss the impact of the inner fitting radius on the accuracy, and determine the outer fitting radius necessary to exclude the signal from neighboring structures. Our method is currently being applied to different Stage III lensing surveys, and can easily be extended to Stage IV lensing surveys.

scholarly journals Probing the primordial power spectrum with cluster number counts

2009 ◽  
Vol 79 (8) ◽  
Author(s):  
Teeraparb Chantavat ◽  
Christopher Gordon ◽  
Joseph Silk
Keyword(s):  
Power Spectrum ◽  
Cluster Number ◽  
Primordial Power Spectrum ◽  
Number Counts

scholarly journals Sufficiency of a Gaussian power spectrum likelihood for accurate cosmology from upcoming weak lensing surveys

2021 ◽  
Author(s):  
Robin E Upham ◽  
Michael L Brown ◽  
Lee Whittaker
Keyword(s):  
Power Spectrum ◽  
Random Noise ◽  
Power Spectra ◽  
Stage Iv ◽  
Wishart Distribution ◽  
Weak Lensing ◽  
Dependence Structure ◽  
Gaussian Fields ◽  
Non Gaussian ◽  
Parameter Constraints

Abstract We investigate whether a Gaussian likelihood is sufficient to obtain accurate parameter constraints from a Euclid-like combined tomographic power spectrum analysis of weak lensing, galaxy clustering and their cross-correlation. Testing its performance on the full sky against the Wishart distribution, which is the exact likelihood under the assumption of Gaussian fields, we find that the Gaussian likelihood returns accurate parameter constraints. This accuracy is robust to the choices made in the likelihood analysis, including the choice of fiducial cosmology, the range of scales included, and the random noise level. We extend our results to the cut sky by evaluating the additional non-Gaussianity of the joint cut-sky likelihood in both its marginal distributions and dependence structure. We find that the cut-sky likelihood is more non-Gaussian than the full-sky likelihood, but at a level insufficient to introduce significant inaccuracy into parameter constraints obtained using the Gaussian likelihood. Our results should not be affected by the assumption of Gaussian fields, as this approximation only becomes inaccurate on small scales, which in turn corresponds to the limit in which any non-Gaussianity of the likelihood becomes negligible. We nevertheless compare against N-body weak lensing simulations and find no evidence of significant additional non-Gaussianity in the likelihood. Our results indicate that a Gaussian likelihood will be sufficient for robust parameter constraints with power spectra from Stage IV weak lensing surveys.

scholarly journals The unequal-time matter power spectrum: impact on weak lensing observables

2021 ◽  
Vol 2021 (08) ◽  
pp. 001
Author(s):  
Lucia F. de la Bella ◽  
Nicolas Tessore ◽  
Sarah Bridle
Keyword(s):  
Power Spectrum ◽  
Weak Lensing ◽  
Matter Power Spectrum

scholarly journals Halo concentrations and weak-lensing number counts in dark energy cosmologies

2003 ◽  
Vol 400 (1) ◽  
pp. 19-19 ◽  
Author(s):  
M. Bartelmann ◽  
F. Perrotta ◽  
C. Baccigalupi
Keyword(s):  
Dark Energy ◽  
Weak Lensing ◽  
Number Counts

scholarly journals Deep learning dark matter map reconstructions from DES SV weak lensing data

2020 ◽  
Vol 492 (4) ◽  
pp. 5023-5029 ◽  
Author(s):  
Niall Jeffrey ◽  
François Lanusse ◽  
Ofer Lahav ◽  
Jean-Luc Starck
Keyword(s):  
Dark Matter ◽  
Deep Learning ◽  
Power Spectrum ◽  
Cosmological Parameters ◽  
Simulated Data ◽  
Wiener Filtering ◽  
Weak Lensing ◽  
Gaussian Shape ◽  
Non Linear ◽  
Mass Mapping

ABSTRACT We present the first reconstruction of dark matter maps from weak lensing observational data using deep learning. We train a convolution neural network with a U-Net-based architecture on over 3.6 × 105 simulated data realizations with non-Gaussian shape noise and with cosmological parameters varying over a broad prior distribution. We interpret our newly created dark energy survey science verification (DES SV) map as an approximation of the posterior mean P(κ|γ) of the convergence given observed shear. Our DeepMass1 method is substantially more accurate than existing mass-mapping methods. With a validation set of 8000 simulated DES SV data realizations, compared to Wiener filtering with a fixed power spectrum, the DeepMass method improved the mean square error (MSE) by 11 per cent. With N-body simulated MICE mock data, we show that Wiener filtering, with the optimal known power spectrum, still gives a worse MSE than our generalized method with no input cosmological parameters; we show that the improvement is driven by the non-linear structures in the convergence. With higher galaxy density in future weak lensing data unveiling more non-linear scales, it is likely that deep learning will be a leading approach for mass mapping with Euclid and LSST.

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