Developing a unified pipeline for large-scale structure data analysis with angular power spectra -- II. A case study for magnification bias and radio continuum surveys

Monthly Notices of the Royal Astronomical Society, Volume 491, Issue 4, February 2020, Pages 4869–4883

Authors:

Konstantinos Tanidis, Stefano Camera, David Parkinson

Abstract:

Following on our purpose of developing a unified pipeline for large-scale structure data analysis with angular power spectra, we now include the weak lensing effect of magnification bias on galaxy clustering in a publicly available, modular parameter estimation code. We thus forecast constraints on the parameters of the concordance cosmological model, dark energy, and modified gravity theories from galaxy clustering tomographic angular power spectra. We find that a correct modelling of magnification is crucial not to bias the parameter estimation, especially in the case of deep galaxy surveys. Our case study adopts specifications of the Evolutionary Map of the Universe, which is a full-sky, deep radio-continuum survey, expected to probe the Universe up to redshift z ∼ 6. We assume the Limber approximation, and include magnification bias on top of density fluctuations and redshift-space distortions. By restricting our analysis to the regime where the Limber approximation holds true, we significantly minimize the computational time needed, compared to that of the exact calculation. We also show that there is a trend for more biased parameter estimates from neglecting magnification when the redshift bins are very wide. We conclude that this result implies a strong dependence on the lensing contribution, which is an integrated effect and becomes dominant when wide redshift bins are considered. Finally, we note that instead of being considered a contaminant, magnification bias encodes important cosmological information, and its inclusion leads to an alleviation of its degeneracy between the galaxy bias and the amplitude normalization of the matter fluctuations.

Reionization history constraints from neural network based predictions of high-redshift quasar continua

(2019)

Authors:

D Ďurovčíková, H Katz, SEI Bosman, FB Davies, J Devriendt, A Slyz

Implications of a transition in the dark energy equation of state for the H-0 and sigma(8) tensions

JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS 2019:12 (2019) 35

Authors:

Ryan E Keeley, Shahab Joudaki, Manoj Kaplinghat, David Kirkby

Abstract:

© 2019 IOP Publishing Ltd and Sissa Medialab. We explore the implications of a rapid appearance of dark energy between the redshifts (z) of one and two on the expansion rate and growth of perturbations. Using both Gaussian process regression and a parametric model, we show that this is the preferred solution to the current set of low-redshift (z<3) distance measurements if H0=73 km s-1 Mpc-1 to within 1% and the high-redshift expansion history is unchanged from the ΛCDM inference by the Planck satellite. Dark energy was effectively non-existent around z=2, but its density is close to the ΛCDM model value today, with an equation of state greater than-1 at z<0.5. If sources of clustering other than matter are negligible, we show that this expansion history leads to slower growth of perturbations at z<1, compared to ΛCDM, that is measurable by upcoming surveys and can alleviate the σ8 tension between the Planck CMB temperature and low-redshift probes of the large-scale structure.

Disconnected pseudo-Cℓ covariances for projected large-scale structure data

Journal of Cosmology and Astroparticle Physics IOP Publishing 2019:11 (2019) 043-043

Authors:

Carlos García-García, David Alonso, Emilio Bellini

Disconnected pseudo-Cℓ covariances for projected large-scale structure data

Journal of Cosmology and Astroparticle Physics IOP Publishing 2019:11 (2019) 043

Authors:

C García-García, D Alonso, Emilio Bellini

Abstract:

The disconnected part of the power spectrum covariance matrix (also known as the "Gaussian" covariance) is the dominant contribution on large scales for galaxy clustering and weak lensing datasets. The presence of a complicated sky mask causes non-trivial correlations between different Fourier/harmonic modes, which must be accurately characterized in order to obtain reliable cosmological constraints. This is particularly relevant for galaxy survey data. Unfortunately, an exact calculation of these correlations involves O(ℓmax6) operations that become computationally impractical very quickly. We present an implementation of approximate methods to estimate the Gaussian covariance matrix of power spectra involving spin-0 and spin-2 flat- and curved-sky fields, expanding on existing algorithms {developed in the context of CMB analyses}. These methods achieve an O(ℓmax3) scaling, which makes the computation of the covariance matrix as fast as the computation of the power spectrum itself. We quantify the accuracy of these methods on large-scale structure and weak lensing data, making use of a large number of Gaussian but otherwise realistic simulations. We show that, using the approximate covariance matrix, we are able to recover the true posterior distribution of cosmological parameters to high accuracy. We also quantify the shortcomings of these methods, which become unreliable on the very largest scales, as well as for covariance matrix elements involving cosmic shear B modes. The algorithms presented here are implemented in the public code NaMaster https://github.com/LSSTDESC/NaMaster.