Browsing by Author "Villaescusa-Navarro, Francisco"

Now showing 1 - 6 of 6
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    Breaking baryon-cosmology degeneracy with the electron density power spectrum
    (IOP Publishing, 2022) Nicola, Andrina; Villaescusa-Navarro, Francisco; Davé, Romeel
    Uncertain feedback processes in galaxies affect the distribution of matter, currently limiting the power of weak lensing surveys. If we can identify cosmological statistics that are robust against these uncertainties, or constrain these effects by other means, then we can enhance the power of current and upcoming observations from weak lensing surveys such as DES, Euclid, the Rubin Observatory, and the Roman Space Telescope. In this work, we investigate the potential of the electron density auto-power spectrum as a robust probe of cosmology and baryonic feedback.
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    Cosmology with a SKA HI intensity mapping survey
    (Proceedings of Science, 2014) Santos, Mario G.; Bull, Philip; Alonso, David; Camera, Stefano; Ferreira, Pedro G.; Bernardi, Gianni; Maartens, Roy; Viel, Matteo; Villaescusa-Navarro, Francisco; Abdalla, Filipe B.; Jarvis, Matt; Metcalf, R. Benton; Pourtsidou, A.; Wolz, Laura
    HI intensity mapping (IM) is a novel technique capable of mapping the large-scale structure of the Universe in three dimensions and delivering exquisite constraints on cosmology, by using HI as a biased tracer of the dark matter density field. This is achieved by measuring the intensity of the redshifted 21cm line over the sky in a range of redshifts without the requirement to resolve individual galaxies. In this chapter, we investigate the potential of SKA1 to deliver HI intensity maps over a broad range of frequencies and a substantial fraction of the sky. By pinning down the baryon acoustic oscillation and redshift space distortion features in the matter power spectrum – thus determining the expansion and growth history of the Universe – these surveys can provide powerful tests of dark energy models and modifications to General Relativity. They can also be used to probe physics on extremely large scales, where precise measurements of spatial curvature and primordial non-Gaussianity can be used to test inflation; on small scales, by measuring the sum of neutrino masses; and at high redshifts where non-standard evolution models can be probed. We discuss the impact of foregrounds as well as various instrumental and survey design parameters on the achievable constraints. In particular we analyse the feasibility of using the SKA1 autocorrelations to probe the large-scale signal.
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    Hiflow: Generating diverse hi maps and inferring cosmology while marginalizing over astrophysics using normalizing flows
    (IOP Publishing, 2022) Hassan, Sultan; Villaescusa-Navarro, Francisco; Wandelt, Benjamin
    A wealth of cosmological and astrophysical information is expected from many ongoing and upcoming large-scale surveys. It is crucial to prepare for these surveys now and develop tools that can efficiently extract most information. We present HIFLOW: a fast generative model of the neutral hydrogen (HI) maps that is conditioned only on cosmology (Ωm and σ8) and designed using a class of normalizing flow models, the masked autoregressive flow. HIFLOW is trained on the state-of-the-art simulations from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project. HIFLOW has the ability to generate realistic diverse maps without explicitly incorporating the expected two-dimensional maps structure into the flow as an inductive bias. We find that HIFLOW is able to reproduce the CAMELS average and standard deviation HI power spectrum within a factor of 2, scoring a very high R2 > 90%. By inverting the flow, HIFLOW provides a tractable high-dimensional likelihood for efficient parameter inference. We show that the conditional HIFLOW on cosmology is successfully able to marginalize over astrophysics at the field level, regardless of the stellar and AGN feedback strengths. This new tool represents a first step toward a more powerful parameter inference, maximizing the scientific return of future HI surveys, and opening a new avenue to minimize the loss of complex information due to data compression down to summary statistics.
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    Inferring halo masses with graph neural networks
    (Institute of Physics, 2022) Villanueva-Domingo, Pablo; Villaescusa-Navarro, Francisco; Dave, Romeel
    Understanding the halo–galaxy connection is fundamental in order to improve our knowledge on the nature and properties of dark matter. In this work, we build a model that infers the mass of a halo given the positions, velocities, stellar masses, and radii of the galaxies it hosts. In order to capture information from correlations among galaxy properties and their phase space, we use Graph Neural Networks (GNNs), which are designed to work with irregular and sparse data. We train our models on galaxies from more than 2000 state-of-the-art simulations from the Cosmology and Astrophysics with MachinE Learning Simulations project. Our model, which accounts for cosmological and astrophysical uncertainties, is able to constrain the masses of the halos with a ∼0.2 dex accuracy. Furthermore, a GNN trained on a suite of simulations is able to preserve part of its accuracy when tested on simulations run with a different code that utilizes a distinct subgrid physics model, showing the robustness of our method.
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    Quijote-png: Simulations of primordial non-gaussianity and the information content of the matter field power spectrum and bispectrum
    (IOP Publishing, 2023) Coulton, William R; Villaescusa-Navarro, Francisco; Karagiannis, Dionysios
    Primordial non-Gaussianity (PNG) is one of the most powerful probes of the early universe, and measurements of the large-scale structure of the universe have the potential to transform our understanding of this area. However, relating measurements of the late-time universe to the primordial perturbations is challenging due to the nonlinear processes that govern the evolution of the universe. To help address this issue, we release a large suite of N-body simulations containing four types of PNG: QUIJOTE-PNG. These simulations were designed to augment the QUIJOTE suite of simulations that explored the impact of various cosmological parameters on large-scale structure observables. Using these simulations, we investigate how much information on PNG can be extracted by extending power spectrum and bispectrum measurements beyond the perturbative regime at z = 0.0. This is the first joint analysis of the PNG and cosmological information content accessible with power spectrum and bispectrum measurements of the nonlinear scales. We find that the constraining power improves significantly up to kmax 0.3 Mpc h » -1 , with diminishing returns beyond as the statistical probes signal-to-noise ratios saturate. This saturation emphasizes the importance of accurately modeling all the contributions to the covariance matrix. Further, we find that combining the two probes is a powerful method of breaking the degeneracies with the ΛCDM parameters.
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    Quijote-png: The information content of the halo power spectrum and bispectrum
    (IOP Publishing, 2023) Coulton, William R; Villaescusa-Navarro, Francisco; Karagiannis, Dionysios
    We investigate how much can be learnt about four types of primordial non-Gaussianity (PNG) from small-scale measurements of the halo field. Using the QUIJOTE-PNG simulations, we quantify the information content accessible with measurements of the halo power spectrum monopole and quadrupole, the matter power spectrum, the halo–matter cross spectrum, and the halo bispectrum monopole. This analysis is the first to include small, nonlinear scales, up to kmax 0.5 h Mpc = -1 , and to explore whether these scales can break degeneracies with cosmological and nuisance parameters making use of thousands of N-body simulations. We perform all the halo measurements in redshift space with a single sample comprised of all halos with mass >3.2 × 1013 h−1 Me. For local PNG, measurements of the scale-dependent bias effect from the power spectrum using sample variance cancellation provide significantly tighter constraints than measurements of the halo bispectrum. In this case measurements of the small scales add minimal additional constraining power. In contrast, the information on equilateral and orthogonal PNG is primarily accessible through the bispectrum. For these shapes, small-scale measurements increase the constraining power of the halo bispectrum by up to 4×, though the addition of scales beyond k ≈ 0.3 h Mpc−1 improves constraints largely through reducing degeneracies between PNG and the other parameters.