119 research outputs found

    Investigating the degeneracy between modified gravity and massive neutrinos with redshift-space distortions

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    There is a well known degeneracy between the enhancement of the growth of large-scale structure produced by modified gravity models and the suppression due to the free-streaming of massive neutrinos at late times. This makes the matter power-spectrum alone a poor probe to distinguish between modified gravity and the concordance Λ\LambdaCDM model when neutrino masses are not strongly constrained. In this work, we investigate the potential of using redshift-space distortions (RSD) to break this degeneracy when the modification to gravity is scale-dependent in the form of Hu-Sawicki f(R)f(R). We find that if the linear growth rate can be recovered from the RSD signal, the degeneracy can be broken at the level of the dark matter field. However, this requires accurate modelling of the non-linearities in the RSD signal, and we here present an extension of the standard perturbation theory-based model for non-linear RSD that includes both Hu-Sawicki f(R)f(R) modified gravity and massive neutrinos.Comment: 24 pages, 12 figures, 1 table; corrected typo in prefactors of the '13'-type 1-loop SPT term

    Deep neural networks to unveil the properties of the cosmic web

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    The main goal of this Thesis work is to test Machine Learning techniques for cosmological analyses. We develop and validate new methods and numerical algorithms to constrain the main parameters of the standard cosmological model, that is Ωm, Ωb, h, ns, σ8, exploiting a likelihood-free inference analysis. The training dataset considered in this work consists of a huge set of second-order and third-order statistics of the dark matter density field, measured from the Quijote N-body simulations [Villaescusa-Navarroet al., 2019]. These are one of the largest sets of dark matter N-body simulations currently available, that span a significant range of the cosmological parameters of the standard model. We implement and train new Neural Networks that can take in input measurements of two-point correlation functions, power spectra and bispectra, and provide in output constraints on the main cosmological parameters. After the training and validation phases, we test the accuracy of our implemented Machine Learning algorithms by processing never-seen-before input datasets generated with cosmological parameters comparable with Planck18 ones [Planck Collaboration et al., 2018]. We find that this statistical procedure can provide robust constraints on some of the aforementioned parameters, in particular Ωm. This Thesis work demonstrates that the considered deep learning techniques based on state-of-the-art Artificial Neural Networks can be effectively employed in cosmological studies, in particular to constrain the main parameters of the cosmological framework by exploiting the statistics of the large-scale structure of the Universe

    Insights into cosmological structure formation with machine learning

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    Our modern understanding of cosmological structure formation posits that small matter density fluctuations present in the early Universe, as traced by the cosmic microwave background, grow via gravitational instability to form extended haloes of dark matter. A theoretical understanding of the structure, evolution and formation of dark matter haloes is an essential step towards unravelling the intricate connection between halo and galaxy formation, needed to test our cosmological model against data from upcoming galaxy surveys. Physical understanding of the process of dark matter halo formation is made difficult by the highly non-linear nature of the haloes' evolution. I describe a new approach to gain physical insight into cosmological structure formation based on machine learning. This approach combines the ability of machine learning algorithms to learn non-linear relationships, with techniques that enable us to physically interpret the learnt mapping. I describe applications of the method, with the aim of investigating which aspects of the early universe density field impact the later formation of dark matter haloes. First I present a case where the process of halo formation is turned into a binary classification problem; the algorithm predicts whether or not dark matter `particles' in the initial conditions of a simulation will collapse into haloes of a given mass range. Second, I present its generalization to regression, where the algorithm infers the final mass of the halo to which each particle will later belong. I show that the initial tidal shear does not play a significant role compared to the initial density field in establishing final halo masses. Finally, I demonstrate that extending the framework to deep learning algorithms such as convolutional neural networks allows us to explore connections between the early universe and late time haloes beyond those studied by existing analytic approximations of halo collapse

    Constraints on alternative cosmological models from clustering and redshift-space distortions

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    In this Thesis I have exploited the most recent observational data from CMB, BAO and growth rate of LSS as well as N-body simulations of modified gravity, to investigate the spatial properties of the large scale structure of Universe by constraining cosmological parameters in the framework of alternative cosmologies. The research is focused on clustering and redshift space distortions as cosmological probe. In this context I have studied the degeneracies between modified gravity and massive neutrinos as well as the robustness of the methodology for constraining the linear growth rate including realistic systematics, implementing suitable parametrizations of the redshift-space distortions in the perspective of current and future galaxy surveys

    More than pupils: Italian women in science at the turn of the twentieth century

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    Cosmological tests of modified gravity models with non-zero gravitational slip

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    Aim of this thesis is to understand how cosmological observations can be used to test departures from general relativity. In particular, we focus on modifications of gravity which predict a non vanishing gravitational slip, i.e. predict that the two scalar potentials appearing in the perturbed metric (in the Newtonian, or Poisson gauge) can differ by a small amount. In GR the only contribution to such a difference comes from the anisotropic stress sourced by matter which, at late time and at linear order, is negligible and the relation between the two potentials is trivial Φ = Ψ. The presence of the gravitational slip affects large scale structures and, in many theories of gravity, it is related to the non-standard propagation of tensor modes. This quantity can be reconstructed in a model independent way by combining different cosmological probes. In this work we parametrize deviations from GR by introducing the phenomenological functions μ(a, k) = G matter /G, Σ(a, k) = Glight /G and η(a, k) = Φ/Ψ. Then, we use the effective field theory formalism of Dark Energy to obtain the analytic expression for these quantities, in the quasi-static approximation, for two classes of theories: Horndeski and beyond Horndeski models. We pay particular attention to the subclasses of these theories which exhibit non-zero gravitational slip, i.e. η ≠1. Since the three phenomenological functions can be reconstructed from cosmological observations, it is possible to constrain these theories and, eventually ruling out some of them, by analysing the analytic expressions for these functions for each class of models. We finally account for the recent strong constraint on the speed of tensor modes from the GW170817 event

    PROBING COSMOLOGY AT DIFFERENT SCALES

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    Although we are in an era of precision cosmology, there is still much about our Universe that we do not know. Moreover, the concordance ΛCDM model of cosmology faces many challenges at all scales relevant to cosmology. Either discrepancies have been discovered with ΛCDM predictions or the model has simply broken down and is not a useful predictor. The current rate of expansion is incorrectly predicted by ΛCDM, as are the size and distribution of dwarf halo galaxies for Milky Way like galaxies. Obtaining the observed cores in galaxies with the standard description of dark matter is troublesome and requires more than the base ΛCDM model, as does understanding star formation and how it impacted galaxy evolution requires much more than base ΛCDM knowledge and many more. My work focuses on probing different scales in cosmology with different techniques to extract information about our Universe and its history. I use ultra-high-energy cosmic-rays (UHECRs) as a probe of the local universe and tested tidal disruption events (TDES) as a possible source of the UHECRs. By analyzing energy requirements, source densities and observed fluxes, I find that TDEs can explain the observed UHECR flux. The assumption of TDEs as the source of UHECRs can lead to a a measurement of the density of super massive black holes which reside in the center of galaxies. At a larger scale, I build a tool to extract the luminosity function of CO from star-forming galaxies with line intensity maps (LIMs) and convolutional neural networks (CNNs). This new technique allows a faster analysis of LIMs in a more model-independent way than previous techniques. Finally, at the largest observable scales, I probe potential dark matter interactions and their impact on the cosmic mi- crowave background (CMB). This work explores how different dark matter interaction mechanisms impact the CMB when considered simultaneously and individually. As cosmology is a science of many scales, all of these scales must be studied to improve our understanding of the Universe. Dong so, my thesis has wide-ranging implications for cosmic-rays, star formation and galaxy evolution, and dark matter interactions
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