Dr Konstantinos Tanidis

The angular power spectrum is a natural tool to analyse the observed galaxy number count fluctuations. In a standard analysis, the angular galaxy distribution is sliced into concentric redshift bins and all correlations of its harmonic coefficients between bin pairs are considered—a procedure referred to as ‘tomography’. However, the unparalleled quality of data from oncoming spectroscopic galaxy surveys for cosmology will render this method computationally unfeasible, given the increasing number of bins. Here, we put to test against synthetic data a novel method proposed in a previous study to save computational time. According to this method, the whole galaxy redshift distribution is subdivided into thick bins, neglecting the cross-bin correlations among them; each of the thick bin is, however, further subdivided into thinner bins, considering in this case all the cross-bin correlations. We create a simulated data set that we then analyse in a Bayesian framework. We confirm that the newly proposed method saves computational time and gives results that surpass those of the standard approach.

In this paper, we apply the multitracer technique to harmonic-space (i.e. angular) power spectra with a likelihood-based approach. This goes beyond the usual Fisher matrix formalism hitherto implemented in forecasts with angular statistics, opening up a window for future developments and direct application to available data sets. We also release a fully operational modified version of the publicly available code CosmoSIS, where we consistently include all the add-ons presented in the previous papers of this series. The result is a modular cosmological parameter estimation suite for angular power spectra of galaxy number counts, allowing for single and multiple tracers, and including density fluctuations, redshift-space distortions, and weak-lensing magnification. We demonstrate the improvement on parameter constraints enabled by the use of multiple tracers on a multitracing analysis of luminous red galaxies and emission-line galaxies. We obtain an enhancement of 44 per cent on the 2σ upper bound on the sum of neutrino masses.

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.

We develop a cosmological parameter estimation code for (tomographic) angular power spectra analyses of galaxy number counts, for which we include, for the first time, redshift-space distortions (RSDs) in the Limber approximation. This allows for a speed-up in computation time, and we emphasize that only angular scales where the Limber approximation is valid are included in our analysis. Our main result shows that a correct modelling of RSD is crucial not to bias cosmological parameter estimation. This happens not only for spectroscopy-detected galaxies, but even in the case of galaxy surveys with photometric redshift estimates. Moreover, a correct implementation of RSD is especially valuable in alleviating the degeneracy between the amplitude of the underlying matter power spectrum and the galaxy bias. We argue that our findings are particularly relevant for present and planned observational campaigns, such as the Euclid satellite or the Square Kilometre Array, which aim at studying the cosmic large-scale structure and trace its growth over a wide range of redshifts and scales.

Cosmological surveys planned for the current decade will provide us with unparalleled observations of the distribution of galaxies on cosmic scales, by means of which we can probe the underlying large-scale structure (LSS) of the Universe. This will allow us to test the concordance cosmological model and its extensions. However, precision pushes us to high levels of accuracy in the theoretical modelling of the LSS observables, in order not to introduce biases in the estimation of cosmological parameters. In particular, effects such as redshift-space distortions (RSD) can become relevant in the computation of harmonic-space power spectra even for the clustering of the photometrically selected galaxies, as it has been previously shown in literature studies. In this work, we investigate the contribution of linear RSD, as formulated in the Limber approximation by arXiv:1902.07226, in forecast cosmological analyses with the photometric galaxy sample of the Euclid survey, in order to assess their impact and quantify the bias on the measurement of cosmological parameters that neglecting such an effect would cause. We perform this task by producing mock power spectra for photometric galaxy clustering and weak lensing, as expected to be obtained from the Euclid survey. We then use a Markov chain Monte Carlo approach to obtain the posterior distributions of cosmological parameters from such simulated observations. We find that neglecting the linear RSD leads to significant biases both when using galaxy correlations alone and when these are combined with cosmic shear, in the so-called 3×2pt approach. Such biases can be as large as 5σ-equivalent when assuming an underlying ΛCDM cosmology. When extending the cosmological model to include the equation-of-state parameters of dark energy, we find that the extension parameters can be shifted by more than 1σ.