Neutrinos and voids in modern cosmology

This thesis deals with the study of the large scale structure in the nonlinear regime. In particular, it focuses on two main topics: the impact of massive neutrinos on the cosmic web and the modeling of void profiles and void bias. Neutrinos are known to be massive particles and thus to participate to the matter content of the Universe and to its evolution. In this era of precision cosmology, the analysis of observational datasets must account for neutrinos, both because cosmology can put strong constraints on the sum of their masses, and because ignoring them can bias the estimation of other cosmological parameters. Having this in mind, we provide a theoretical model to describe the nonlinear matter power spectrum in massive neutrino cosmologies. This model is obtained by generalizing the already existing halo model, to account for the presence of massive neutrinos. Then, we also discuss the clustering of galaxies in the same framework and provide a comparison with N-body simulations. A promising environment where to study neutrinos is represented by cosmic voids. We perform a numerical analysis of statistical properties of voids, identified both in \u39bCDM and massive neutrino cosmologies. The aim of this project is to understand how neutrinos change the void properties and which of them are more sensitive to their presence. This is the starting point for thinking about constraining neutrino masses using cosmic voids. Voids are very interesting objects, that have been studied much less than halos and clusters. We present here a model for describing the void density profile. In particular, we present different models describing the abundance and spatial distribution of both halos and voids in the Lagrangian field, and explain how they can be applied to compute density profiles. Then, we evolve these Lagrangian profiles to the Eulerian space, where actual measurements are performed. We discuss the evolution described by the spherical model and the Zel\u2019dovich approximations. Since the density profile around tracers is the cross-correlation between the tracers and the matter field, this quantity is sensitive to the bias of tracers with respect to the matter field. We discuss the void linear bias in Lagrangian and Eulerian space, and how it differs from the linear bias of halos

Cosmology and neutrino mass with the minimum spanning tree

The information content of the minimum spanning tree (MST), used to capture higher order statistics and information from the cosmic web, is compared to that of the power spectrum for a CDM model. The measurements are made in redshift space using haloes from the Quijote simulation of mass ≥ 3.2× 1013, h-1,M⊙ in a box of length Lbox=1h-1, Gpc. The power spectrum multipoles (monopole and quadrupole) are computed for Fourier modes in the range 0.006, hMpc-1< k < 0.5, hMpc-1. For comparison the MST is measured with a minimum length-scale of lmin≃13, h-1, Mpc. Combining the MST and power spectrum allows for many of the individual degeneracies to be broken; on its own the MST provides tighter constraints on the sum of neutrino masses Mν and cosmological parameters h, ns, an

Cosmological Information in Skew Spectra of Biased Tracers in Redshift Space

Extracting the non-Gaussian information encoded in the higher-order clustering statistics of the large-scale structure is key to fully realizing the potential of upcoming galaxy surveys. We investigate the information content of the redshift-space {\it weighted skew spectra} of biased tracers as efficient estimators for 3-point clustering statistics. The skew spectra are constructed by correlating the observed galaxy field with an appropriately-weighted square of it. We perform numerical Fisher forecasts using two synthetic datasets; the halo catalogs from the Quijote N-body simulations and the galaxy catalogs from the Molino suite. The latter serves to understand the effect of marginalization over a more complex matter-tracer biasing relation. Compared to the power spectrum multipoles, we show that the skew spectra substantially improve the constraints on six parameters of the $\nu\Lambda$CDM model, $\{\Omega_m, \Omega_b, h, n_s, \sigma_8, M_\nu\}$. Imposing a small-scale cutoff of $k_{\rm max}=0.25 \, {\rm Mpc}^{-1}h$, the improvements from skew spectra alone range from 23% to 62% for the Quijote halos and from 32% to 71% for the Molino galaxies. Compared to the previous analysis of the bispectrum monopole on the same data and using the same range of scales, the skew spectra of Quijote halos provide competitive constraints. Conversely, the skew spectra outperform the bispectrum monopole for all cosmological parameters for the Molino catalogs. This may result from additional anisotropic information, particularly enhanced in the Molino sample, that is captured by the skew spectra but not by the bispectrum monopole. Our stability analysis of the numerical derivatives shows comparable convergence rates for the power spectrum and the skew spectra, indicating potential underestimation of parameter uncertainties by at most 30%.Comment: 43 pages, 25 figure

Measurements of cosmic expansion and growth rate of structure from voids in the Sloan Digital Sky Survey between redshift 0.07 and 1.0

We present measurements of the anisotropic cross-correlation of galaxies and cosmic voids in data from the Sloan Digital Sky Survey Main Galaxy Sample (MGS), Baryon Oscillation Spectroscopic Survey (BOSS) and extended BOSS (eBOSS) luminous red galaxy catalogues from SDSS Data Releases 7, 12 and 16, covering the redshift range $0.07<z<1.0$. As in our previous work analysing voids in subsets of these data, we use a reconstruction method applied to the galaxy data before void-finding in order to remove selection biases when constructing the void samples. We report results of a joint fit to the multipole moments of the measured cross-correlation for the growth rate of structure, $f\sigma_8(z)$, and the ratio $D_\mathrm{M}(z)/D_\mathrm{H}(z)$ of the comoving angular diameter distance to the Hubble distance, in six redshift bins. For $D_\mathrm{M}/D_\mathrm{H}$, we are able to achieve a significantly higher precision than that obtained from analyses of the baryon acoustic oscillations (BAO) and galaxy clustering in the same datasets. Our growth rate measurements are of lower precision but still comparable with galaxy clustering results. For both quantities, the results agree well with the expectations for a $\Lambda$CDM model. Assuming a flat Universe, our results correspond to a measurement of the matter density parameter $\Omega_\mathrm{m}=0.337^{+0.026}_{-0.029}$. For more general models the degeneracy directions obtained are consistent with and complementary to those from other cosmological probes. These results consolidate void-galaxy cross-correlation measurements as a pillar of modern observational cosmology.Comment: Author's accepted manuscript. 17 Pages, 8 Figures. MNRAS (2022

Robust Simulation-Based Inference in Cosmology with Bayesian Neural Networks

Simulation-based inference (SBI) is rapidly establishing itself as a standard machine learning technique for analyzing data in cosmological surveys. Despite continual improvements to the quality of density estimation by learned models, applications of such techniques to real data are entirely reliant on the generalization power of neural networks far outside the training distribution, which is mostly unconstrained. Due to the imperfections in scientist-created simulations, and the large computational expense of generating all possible parameter combinations, SBI methods in cosmology are vulnerable to such generalization issues. Here, we discuss the effects of both issues, and show how using a Bayesian neural network framework for training SBI can mitigate biases, and result in more reliable inference outside the training set. We introduce cosmoSWAG, the first application of Stochastic Weight Averaging to cosmology, and apply it to SBI trained for inference on the cosmic microwave background.Comment: 5 pages, 3 figures. Accepted at the ML4Astro Machine Learning for Astrophysics Workshop at the Thirty-ninth International Conference on Machine Learning (ICML 2022

Beyond two-point statistics: using the minimum spanning tree as a tool for cosmology

Cosmological studies of large-scale structure have relied on two-point statistics, not fully exploiting the rich structure of the cosmic web. In this paper we show how to capture some of this cosmic web information by using the minimum spanning tree (MST), for the first time using it to estimate cosmological parameters in simulations. Discrete tracers of dark matter such as galaxies, N-body particles or haloes are used as nodes to construct a unique graph, the MST, that traces skeletal structure. We study the dependence of the MST on cosmological parameters using haloes from a suite of COmoving Lagrangian Acceleration (COLA) simulations with a box size of 250 h(-1) Mpc, varying the amplitude of scalar fluctuations (A(s)), matter density (Omega(m)), and neutrino mass (Sigma m(nu)). The power spectrum P and bispectrum B are measured for wavenumbers between 0.125 and 0.5 h Mpc(-1), while a corresponding lower cut of similar to 12.6 h(-1) Mpc is applied to the MST. The constraints from the individual methods are fairly similar but when combined we see improved 1 sigma constraints of similar to 17 per cent (similar to 12 per cent) on Omega(m) and similar to 12 per cent (similar to 10 per cent) on A(s) with respect to P (P + B) thus showing the MST is providing additional information. The MST can be applied to current and future spectroscopic surveys (BOSS, DESI, Euclid, PSF, WFIRST, and 4MOST) in 3D and photometric surveys (DES and LSST) in tomographic shells to constrain parameters and/or test systematics

Cosmological measurements from void-galaxy and galaxy-galaxy clustering in the Sloan Digital Sky Survey

We present the cosmological implications of measurements of void-galaxy and galaxy-galaxy clustering from the Sloan Digital Sky Survey (SDSS) Main Galaxy Sample (MGS), Baryon Oscillation Spectroscopic Survey (BOSS), and extended BOSS (eBOSS) luminous red galaxy catalogues from SDSS Data Release 7, 12, and 16, covering the redshift range $0.07 < z < 1.0$. We fit a standard $\Lambda$CDM cosmological model as well as various extensions including a constant dark energy equation of state not equal to $-1$, a time-varying dark energy equation of state, and these same models allowing for spatial curvature. Results on key parameters of these models are reported for void-galaxy and galaxy-galaxy clustering alone, both of these combined, and all these combined with measurements from the cosmic microwave background (CMB) and supernovae (SN). For the combination of void-galaxy and galaxy-galaxy clustering plus CMB and SN, we find tight constraints of $\Omega_\mathrm{m} = 0.3127\pm 0.0055$ for a base $\Lambda$CDM cosmology, $\Omega_\mathrm{m} = 0.3172\pm 0.0061, w = -0.930\pm 0.039$ additionally allowing the dark energy equation of state $w$ to vary, and $\Omega_\mathrm{m} = 0.3239\pm 0.0085, w = -0.889\pm 0.052, \mathrm{and}\ \Omega_\mathrm{k} = -0.0031\pm 0.0028$ further extending to non-flat models.Comment: 11 pages, 9 figures. Submitted to MNRA