Nuevas técnicas para explotar cosmológicamente cartografiados de galaxias de forma óptima

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Físicas, Departamento de Astrofísica y Ciencias de la Atmósfera, leída el 27/07/2017The emergence of the current cosmological model, which we know as Lambda Cold Dark Matter(ΛCDM), and the sophistication of the experiments with which we observe the Universe enabled the actual era of precision cosmology. This is because thedevelopment of technology together with the accurate predictions of multiple cosmological observables by ΛCDM opened the possibility of determining the properties of the Universe in detail. Nonetheless, there are still some challenges in this model, e.g. to unveil the nature of Dark Matter(DM) and to explain the cause behind the accelerated expansion of the universe. To enlighten these issues, tens of observatories and scientific instruments are built all over the world, and they will produce newer and preciser galaxy surveys. These future surveys will sample large volumes of the universe with unprecedented precision. The cosmological information extracted from them will no longer be dominated by statistical errors, and thus systematic errors will become the main source of uncertainty. These errors arise from an incorrect interpretation of the data and/or an imprecise modelling of the effect of observational techniques on the results. Moreover, they will have to be correctly accounted for in order to unbiasedly obtain cosmological information from future surveys and to fully exploit them...El desarrollo del modelo cosmológico actual, que denominamos Lambda Cold Dark Matter (ΛCDM), y la evolución de los experimentos con los que observamos el Universo iniciaron la actual era de la cosmología de precisión. Esto es debido a que ΛCDM realiza predicciones detalladas de diferentes observables en el universo cercano y lejano, lo que unido al progreso en la técnica, habilitó determinar detalladamente las propiedades del cosmos. Sin embargo, este modelo cuenta todavía con algunos problemas abiertos, como encontrar la naturaleza de la Materia Oscura (DM, Dark Matter) o descubrir qué produce la expansión acelerada del universo. En aras de solventarlos, decenas de observatorios astronómicos e instrumentos científicos se están construyendo por todo el mundo, y estos posibilitarán nuevos y más precisos cartografiados de galaxias. Estos futuros cartografiados mapearán con exquisito detalle grandes volúmenes cosmológicos, lo que les permitirá, al extraer información cosmológica de las observaciones, reducir al mínimo las incertidumbres estadísticas. Así, los errores sistemáticos se convertirán en la principal fuente de inexactitud, donde estos emergen de interpretar erróneamente los datos o de desconocer de forma específica el impacto de las técnicas observacionales en ellos. Además, estas inexactitudes deben ser correctamente modelados para no introducir sesgos al extraer parámetros cosmológicos de los cartografiados y aprovecharlos de forma óptima...Depto. de Física de la Tierra y AstrofísicaFac. de Ciencias FísicasTRUEunpu

Relativistic Angular Redshift Fluctuations embedded in Large Scale Varying Gravitational Potentials

We compute the linear order, general relativistic corrections to angular redshift fluctuations (ARF), a new cosmological observable built upon density-weighted two-dimensional (2D) maps of galaxy redshifts. We start with an existing approach for galaxy/source counts developed in the Newtonian gauge, and generalize it to ARF, modifying for this purpose a standard Boltzmann code. Our calculations allow us identifying the velocity terms as the leading corrections on large scales, emphasizing the sensitivity of ARF to peculiar, cosmological velocity fields. Just like for standard 2D clustering, the impact of gravitational lensing on ARF is dominant on small angular scales and for wide redshift shells, while the signatures associated to gravitational potentials are extremely small and hardly detectable. The ARF also present interesting correlation properties to anisotropies of the Cosmic Microwave Background (CMB): they are highly correlated to CMB lensing potential fluctuations, while also exhibiting a significant (S/N$\sim 4$-$5$) {\em anti-}correlation with the Integrated Sachs-Wolfe effect (ISW). This negative ARF$\times$ISW signal is quite complementary to the standard 2D clustering$\times$ISW correlation, since the former appears mostly at higher redshift ($z\sim 2$) than the latter ($z\lesssim 1)$, and the combination of the two observables significantly increases the $\chi^2$ statistics testing the null (no ISW) hypothesis. We conclude that ARF constitute a novel, alternative, and potentially powerful tool to constrain the nature of Dark Energy component that gives rise to the ISW.Comment: 25 pages, 7 figures, comments welcome

A neural network emulator for the Lyman-α\alpha 1D flux power spectrum

The Lyman-$\alpha$ offers a unique avenue for studying the distribution of matter in the high redshift universe and extracting precise constraints on the nature of dark matter, neutrino masses, and other $\Lambda$CDM extensions. However, interpreting this observable requires accurate modelling of the thermal and ionisation state of the intergalactic medium, and therefore resorting to computationally expensive hydrodynamical simulations. In this work, we build a neural network that serves as a surrogate model for rapid predictions of the one-dimensional \lya flux power spectrum ($P_{\rm 1D}$), thereby making Bayesian inference feasible for this observable. Our emulation technique is based on modelling $P_{\rm 1D}$ as a function of the slope and amplitude of the linear matter power spectrum rather than as a function of cosmological parameters. We show that our emulator achieves sub-percent precision across the full range of scales ($k_{\parallel }=0.1$ to 4Mpc$^{-1}$) and redshifts ($z=2$ to 4.5) considered, and also for three $\Lambda$CDM extensions not included in the training set: massive neutrinos, running of the spectral index, and curvature. Furthermore, we show that it performs at the 1% level for ionisation and thermal histories not present in the training set and performs at the percent level when emulating down to $k_{\parallel}$=8Mpc$^{-1}$. These results affirm the efficacy of our emulation strategy in providing accurate predictions even for cosmologies and reionisation histories that were not explicitly incorporated during the training phase, and we expect it to play a critical role in the cosmological analysis of the DESI survey.Comment: 15 pages, 17 figure

Diffstar: A Fully Parametric Physical Model for Galaxy Assembly History

We present Diffstar, a smooth parametric model for the in-situ star formation history (SFH) of galaxies. Diffstar is distinct from conventional SFH models that are used to interpret the spectral energy distribution (SED) of an observed galaxy, because our model is parametrized directly in terms of basic features of galaxy formation physics. The Diffstar model assumes that star formation is fueled by the accretion of gas into the dark matter halo of the galaxy, and at the foundation of Diffstar is a parametric model for halo mass assembly, Diffmah. We include parametrized ingredients for the fraction of accreted gas that is eventually transformed into stars, $\epsilon_{\rm ms},$ and for the timescale over which this transformation occurs, $\tau_{\rm cons};$ some galaxies in Diffstar experience a quenching event at time $t_{\rm q},$ and may subsequently experience rejuvenated star formation. We fit the SFHs of galaxies predicted by the IllustrisTNG (TNG) and UniverseMachine (UM) simulations with the Diffstar parameterization, and show that our model is sufficiently flexible to describe the average stellar mass histories of galaxies in both simulations with an accuracy of $\sim0.1$ dex across most of cosmic time. We use Diffstar to compare TNG to UM in common physical terms, finding that: (i) star formation in UM is less efficient and burstier relative to TNG; (ii) galaxies in UM have longer gas consumption timescales, $\tau_{\rm cons}$, relative to TNG; (iii) rejuvenated star formation is ubiquitous in UM, whereas quenched TNG galaxies rarely experience sustained rejuvenation; and (iv) in both simulations, the distributions of $\epsilon_{\rm ms}$, $\tau_{\rm cons}$, and $t_{\rm q}$ share a common characteristic dependence upon halo mass, and present significant correlations with halo assembly history. [Abridged]Comment: 26 pages, 21 figure

Machine learning synthetic spectra for probabilistic redshift estimation: SYTH-Z

Photometric redshift estimation algorithms are often based on representative data from observational campaigns. Data-driven methods of this type are subject to a number of potential deficiencies, such as sample bias and incompleteness. Motivated by these considerations, we propose using physically motivated synthetic spectral energy distributions in redshift estimation. In addition, the synthetic data would have to span a domain in colour-redshift space concordant with that of the targeted observational surveys. With a matched distribution and realistically modelled synthetic data in hand, a suitable regression algorithm can be appropriately trained; we use a mixture density network for this purpose. We also perform a zero-point re-calibration to reduce the systematic differences between noise-free synthetic data and the (unavoidably) noisy observational data sets. This new redshift estimation framework, SYTH-Z, demonstrates superior accuracy over a wide range of redshifts compared to baseline models trained on observational data alone. Approaches using realistic synthetic data sets can therefore greatly mitigate the reliance on expensive spectroscopic follow-up for the next generation of photometric surveys.Comment: 14 pages, 8 figure

Consistent and simultaneous modelling of galaxy clustering and galaxy-galaxy lensing with Subhalo Abundance Matching

The spatial distribution of galaxies and their gravitational lensing signal offer complementary tests of galaxy formation physics and cosmology. However, their synergy can only be fully exploited if both probes are modelled accurately and consistently. In this paper, we demonstrate that this can be achieved using an extension of Subhalo Abundance Matching, dubbed SHAMe. Specifically, we use mock catalogues built from the TNG300 hydrodynamical simulation to show that SHAMe can simultaneously model the multipoles of the redshift-space galaxy correlation function and galaxy-galaxy lensing, without noticeable bias within the statistical sampling uncertainties of a SDSS volume and on scales r = [0.6-30] Mpc/h. Modelling the baryonic processes in galaxy-galaxy lensing with a baryonification scheme allows SHAMe's range of validity to be extended to r = [0.1-30] Mpc/h. Remarkably, our model achieves this level of precision with just five free parameters beyond those describing the baryonification model. At fixed cosmology, we find that galaxy-galaxy lensing provides a general consistency test but little additional information on galaxy modelling parameters beyond that encoded in the redshift-space multipoles. It does, however, improve constraints if only the projected correlation function is available, as in surveys with only photometric redshifts. We expect SHAMe to have a higher fidelity across a wider range of scales than more traditional methods such as Halo Occupation Distribution modelling. Thus it should provide a significantly more powerful and more robust tool for analysing next-generation large-scale surveys.Comment: 14 pages, 7 figures. Submitted to MNRA

The cosmology dependence of the concentration-mass-redshift relation

The concentrations of dark matter haloes provide crucial information about their internal structure and how it depends on mass and redshift -- the so-called concentration-mass-redshift relation, denoted $c(M,z)$. We present here an extensive study of the cosmology-dependence of $c(M,z)$ that is based on a suite of 72 gravity-only, full N-body simulations in which the following cosmological parameters were varied: $\sigma_{8}$, $\Omega_{\mathrm{M}}$, $\Omega_{\mathrm{b}}$, $n_{\mathrm{s}}$, $h$, $M_{\nu}$, $w_{0}$ and $w_{\mathrm{a}}$. We characterize the impact of these parameters on concentrations for different halo masses and redshifts. In agreement with previous works, and for all cosmologies studied, we find that there exists a tight correlation between the characteristic densities of dark matter haloes within their scale radii, $r_{-2}$, and the critical density of the Universe at a suitably defined formation time. This finding, when combined with excursion set modelling of halo formation histories, allows us to accurately predict the concentrations of dark matter haloes as a function of mass, redshift, and cosmology. We use our simulations to test the reliability of a number of published models for predicting halo concentration and highlight when they succeed or fail to reproduce the cosmological $c(M,z)$ relation.Comment: 11 pages, 9 figure