Dark Energy from the log-transformed convergence field

Abstract

A logarithmic transform of the convergence field improves `the information content', ie., the overall precision associated with the measurement of the amplitude of the convergence power spectrum by improving the covariance matrix properties. The translation of this improvement in the information content to that in cosmological parameters, such as those associated with dark energy, requires knowing the sensitivity of the log-transformed field to those cosmological parameters. In this paper we use N-body simulations with ray tracing to generate convergence fields at multiple source redshifts as a function of cosmology. The gain in information associated with the log-transformed field does lead to tighter constraints on dark energy parameters, but only if shape noise is neglected. The presence of shape noise quickly diminishes the advantage of the log mapping, more quickly than we would expect based on the information content. With or without shape noise, using a larger pixel size allows for a more efficient log-transformation.

12 pages, 10 figures, 3 tables. Submitted to ApJ

References in corpus (10)

• Why your model parameter confidences might be too optimistic -- unbiased estimation of the inverse covariance matrix
• Simulations of Baryon Acoustic Oscillations II: Covariance matrix of the matter power spectrum
• Cosmological Information in Weak Lensing Peaks
• Shear Power Spectrum Reconstruction using Pseudo-Spectrum Method
• Rejuvenating Power Spectra II: the Gaussianized galaxy density field
• Re-capturing cosmic information
• Gaussianizing the non-Gaussian lensing convergence field I: the performance of the Gaussianization
• Non-linear weak lensing forecasts
• Increasing the Fisher Information Content in the Matter Power Spectrum by Non-linear Wavelet Weiner Filtering
• Removable Matter-Power-Spectrum Covariance from Bias Fluctuations

Cited by in corpus (21)

• Improving lognormal models for cosmological fields
• Joint analysis of cluster number counts and weak lensing power spectrum to correct for the super-sample covariance
• Euclid: impact of nonlinear prescriptions on cosmological parameter estimation from weak lensing cosmic shear
• On the inadequacy of N-point correlation functions to describe nonlinear cosmological fields: explicit examples and connection to simulations
• Impact of the non-Gaussian covariance of the weak lensing power spectrum and bispectrum on cosmological parameter estimation
• Direct Minkowski Functional analysis of large redshift surveys: a new high--speed code tested on the luminous red galaxy Sloan Digital Sky Survey-DR7 catalogue
• The Accuracy of Weak Lensing Simulations
• The effect of baryons in the cosmological lensing PDFs
• Denoising Weak Lensing Mass Maps with Deep Learning
• Optimal non-linear transformations for large scale structure statistics
• Sufficient observables for large scale structure in galaxy surveys
• Tomographic weak lensing shear spectra from large N-body and hydrodynamical simulations
• An optimal survey geometry of weak lensing survey: minimizing super-sample covariance
• New fitting formula for cosmic non-linear density distribution
• Modeling of weak lensing statistics. I. Power spectrum and bispectrum
• On the total cosmological information in galaxy clustering: an analytical approach
• Fast generation of weak lensing maps by the inverse-Gaussianization method
• Gaussianizing the non-Gaussian lensing convergence field II: the applicability to noisy data
• Enhancing the Cosmic Shear Power Spectrum
• The Weak Lensing Peak Statistics in the Mocks by the inverse-Gaussianization Method
• Neural style transfer of weak lensing mass maps