scholarly journals Impact of magnification and size bias on the weak lensing power spectrum and peak statistics

2014 ◽  
Vol 89 (2) ◽  
Author(s):  
Jia Liu ◽  
Zoltán Haiman ◽  
Lam Hui ◽  
Jan M. Kratochvil ◽  
Morgan May
Keyword(s):  
Power Spectrum ◽  
Weak Lensing ◽  
Size Bias

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 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.

scholarly journals OPTIMAL CAPTURE OF NON-GAUSSIANITY IN WEAK-LENSING SURVEYS: POWER SPECTRUM, BISPECTRUM, AND HALO COUNTS

2010 ◽  
Vol 712 (2) ◽  
pp. 992-1002 ◽  
Author(s):  
Joel Bergé ◽  
Adam Amara ◽  
Alexandre Réfrégier
Keyword(s):  
Power Spectrum ◽  
Weak Lensing

scholarly journals Baryon impact on weak lensing peaks and power spectrum: Low-bias statistics and self-calibration in future surveys

2013 ◽  
Vol 87 (2) ◽  
Author(s):  
Xiuyuan Yang ◽  
Jan M. Kratochvil ◽  
Kevin Huffenberger ◽  
Zoltán Haiman ◽  
Morgan May
Keyword(s):  
Power Spectrum ◽  
Weak Lensing ◽  
Self Calibration

scholarly journals General relativistic corrections to the weak lensing convergence power spectrum

2017 ◽  
Vol 96 (10) ◽  
Author(s):  
John T. Giblin ◽  
James B. Mertens ◽  
Glenn D. Starkman ◽  
Andrew R. Zentner
Keyword(s):  
Power Spectrum ◽  
Weak Lensing ◽  
Relativistic Corrections ◽  
General Relativistic
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