arXiv subjects

P. Baratta

Publications and source records attributed to P. Baratta.

5 recordsLinked to original sources

arXiv2026

Euclid preparation. CII. Non-Gaussianity of 2-pt statistics likelihood: Parameter inference with a non-Gaussian likelihood in Fourier and configuration space

In this work we account for this skewness in parameter inference by modelling the likelihood through an Edgeworth expansion which involves the complete skewness tensor, composed of 1-point, 2-point, and 3-point correlators. To simplify the calculations of this expansion we perform a change of basis which reduces the precision matrix to the identity. In this basis, the off-diagonal elements of the skewness tensor are consistent with zero, while the amplitude of its diagonal match the level expected for a Gaussian underlying field. We perform parameter inference with this likelihood model and find that including only the diagonal part of the skewness is sufficient, while incorporating the full skewness tensor injects noise without improving accuracy. Despite the estimated excess skewness in the original basis, the cosmological constraints remain effectively unchanged when adopting a Gaussian likelihood or considering the more complete Edgeworth expansion, with variations in the figure of merit of cosmological parameters between the two cases below $5\%$. This result remains unchanged against variations of the survey volume and geometry, scale-cut, and 2-point statistic (power spectrum or correlation function). Using $10\, 000$ cloned \Euclid large mocks based on realistic galaxy catalogues with characteristics close to future \Euclid data, we find no detectable excess skewness on intermediate scales, due to the level of shot noise expected for the \Euclid spectroscopic sample. We conclude that the Gaussian likelihood assumption is robust for \Euclid 2-point statistics analyses in both Fourier and configuration space.

astro-ph.CO↗

arXiv2025

Euclid preparation: LXXXVII. Non-Gaussianity of 2-point statistics likelihood: Precise analysis of the matter power spectrum distribution

We investigate the non-Gaussian features in the distribution of the matter power spectrum multipoles. Using the COVMOS method, we generate 100\,000 mock realisations of dark matter density fields in both real and redshift space across multiple redshifts and cosmological models. We derive an analytical framework linking the non-Gaussianity of the power spectrum distribution to higher-order statistics of the density field, including the trispectrum and pentaspectrum. We explore the effect of redshift-space distortions, the geometry of the survey, the Fourier binning, the integral constraint, and the shot noise on the skewness of the distribution of the power spectrum measurements. Our results demonstrate that the likelihood of the estimated matter power spectrum deviates significantly from a Gaussian assumption on nonlinear scales, particularly at low redshift. This departure is primarily driven by the pentaspectrum contribution, which dominates over the trispectrum at intermediate scales. We also examine the impact of the finiteness of the survey geometry in the context of the Euclid mission and find that both the shape of the survey and the integral constraint amplify the skewness.

astro-ph.CO↗

arXiv2023

Euclid preparation. LII. Forecast impact of super-sample covariance on 3x2pt analysis with Euclid

Deviations from Gaussianity in the distribution of the fields probed by large-scale structure surveys generate additional terms in the data covariance matrix, increasing the uncertainties in the measurement of the cosmological parameters. Super-sample covariance (SSC) is among the largest of these non-Gaussian contributions, with the potential to significantly degrade constraints on some of the parameters of the cosmological model under study - especially for weak lensing cosmic shear. We compute and validate the impact of SSC on the forecast uncertainties on the cosmological parameters for the Euclid photometric survey, obtained with a Fisher matrix analysis, both considering the Gaussian covariance alone and adding the SSC term - computed through the public code $\tt{PySSC}$. The photometric probes are considered in isolation and combined in the '3$\times$2pt' analysis. We find the SSC impact to be non-negligible - halving the Figure of Merit of the dark energy parameters $(w_0, w_a)$ in the 3$\times$2pt case and substantially increasing the uncertainties on $\Omega_{{\rm m}, 0}, w_0$, and $\sigma_8$ for cosmic shear; photometric galaxy clustering, on the other hand, is less affected due to the lower probe response. The relative impact of SSC does not show significant changes under variations of the redshift binning scheme, while it is smaller for weak lensing when marginalising over the multiplicative shear bias nuisance parameters, which also leads to poorer constraints on the cosmological parameters. Finally, we explore how the use of prior information on the shear and galaxy bias changes the SSC impact. Improving shear bias priors does not have a significant impact, while galaxy bias must be calibrated to sub-percent level to increase the Figure of Merit by the large amount needed to achieve the value when SSC is not included.

astro-ph.CO↗

arXiv2023

Cosmological inference including massive neutrinos from the matter power spectrum: biases induced by uncertainties in the covariance matrix

Data analysis from upcoming large galaxy redshift surveys, such as Euclid and DESI will significantly improve constraints on cosmological parameters. To optimally extract the information from these galaxy surveys, it is important to control with a high level of confidence the uncertainty and bias arising from the estimation of the covariance that affects the inference of cosmological parameters. In this work, we are addressing two different but closely related issues: (i) the sampling noise present in a covariance matrix estimated from a finite set of simulations and (ii) the impact on cosmological constraints of the non-Gaussian contribution to the covariance matrix of the power spectrum. We focus on the parameter estimation obtained from fitting the matter power spectrum in real space, using the DEMNUni N-body simulations. Regarding the first issue, we adopt two different approaches to reduce the sampling noise in the precision matrix that propagates in the parameter space: on the one hand using an alternative estimator of the covariance matrix based on a non-linear shrinkage, NERCOME; and on the other hand employing a method of fast generation of approximate mock catalogs, COVMOS. We find that NERCOME can significantly reduce the noise induced on the posterior distribution of parameters, but at the cost of a systematic overestimation of the error bars on the cosmological parameters. We show that using a COVMOS covariance matrix estimated from a large number of realisations (10~000) results in unbiased cosmological constraints. Regarding the second issue, we quantify the impact on cosmological constraints of the non-Gaussian part of the power spectrum covariance purely coming from non-linear clustering. We find that when this term is neglected, both the errors and central values of the estimated parameters are affected for a scale cut $\kmax > 0.2\ \invMpc$.

astro-ph.CO↗

arXiv2021

Impact of survey geometry and super-sample covariance on future photometric galaxy surveys

Photometric galaxy surveys probe the late-time Universe where the density field is highly non-Gaussian. A consequence is the emergence of the super-sample covariance (SSC), a non-Gaussian covariance term that is sensitive to fluctuations on scales larger than the survey window. In this work, we study the impact of the survey geometry on the SSC and, subsequently, on cosmological parameter inference. We devise a fast SSC approximation that accounts for the survey geometry and compare its performance to the common approximation of rescaling the results by the fraction of the sky covered by the survey, $f_\mathrm{SKY}$, dubbed 'full-sky approximation'. To gauge the impact of our new SSC recipe, dubbed 'partial-sky', we perform Fisher forecasts on the parameters of the $(w_0,w_a)$-CDM model in a 3x2 points analysis, varying the survey area, the geometry of the mask and the galaxy distribution inside our redshift bins. The differences in the marginalised forecast errors, with the full-sky approximation performing poorly for small survey areas but excellently for stage-IV-like areas, are found to be absorbed by the marginalisation on galaxy bias nuisance parameters. For large survey areas, the unmarginalised errors are underestimated by about 10% for all probes considered. This is a hint that, even for stage-IV-like surveys, the partial-sky method introduced in this work will be necessary if tight priors are applied on these nuisance parameters.

astro-ph.CO↗