Semi-blind Bayesian inference of CMB map and power spectrum

We present a new blind formulation of the Cosmic Microwave Background (CMB) inference problem. The approach relies on a phenomenological model of the multi-frequency microwave sky without the need for physical models of the individual components. For all-sky and high resolution data, it unifies parts of the analysis that have previously been treated separately, such as component separation and power spectrum inference. We describe an efficient sampling scheme that fully explores the component separation uncertainties on the inferred CMB products such as maps and/or power spectra. External information about individual components can be incorporated as a prior giving a flexible way to progressively and continuously introduce physical component separation from a maximally blind approach. We connect our Bayesian formalism to existing approaches such as Commander, SMICA and ILC, and discuss possible future extensions.Comment: 11 pages, 9 figure

QUIJOTE scientific results -- XIII. Intensity and polarization study of supernova remnants in the QUIJOTE-MFI wide survey: CTB 80, Cygnus Loop, HB 21, CTA 1, Tycho and HB 9

We use the new QUIJOTE-MFI wide survey (11, 13, 17 and 19 GHz) to produce spectral energy distributions (SEDs), on an angular scale of 1 deg, of the supernova remnants (SNRs) CTB 80, Cygnus Loop, HB 21, CTA 1, Tycho and HB 9. We provide new measurements of the polarized synchrotron radiation in the microwave range. For each SNR, the intensity and polarization SEDs are obtained and modelled by combining QUIJOTE-MFI maps with ancillary data. In intensity, we confirm the curved power law spectra of CTB 80 and HB 21 with a break frequency $\nu_{\rm b}$ at 2.0$^{+1.2}_{-0.5}$ GHz and 5.0$^{+1.2}_{-1.0}$ GHz respectively; and spectral indices respectively below and above the spectral break of $-0.34\pm0.04$ and $-0.86\pm0.5$ for CTB 80, and $-0.24\pm0.07$ and $-0.60\pm0.05$ for HB 21. In addition, we provide upper limits on the Anomalous Microwave Emission (AME), suggesting that the AME contribution is negligible towards these remnants. From a simultaneous intensity and polarization fit, we recover synchrotron spectral indices as flat as $-0.24$, and the whole sample has a mean and scatter of $-0.44\pm0.12$. The polarization fractions have a mean and scatter of $6.1\pm1.9$\%. When combining our results with the measurements from other QUIJOTE studies of SNRs, we find that radio spectral indices are flatter for mature SNRs, and particularly flatter for CTB 80 ($-0.24^{+0.07}_{-0.06}$) and HB 21 ($-0.34^{+0.04}_{-0.03}$). In addition, the evolution of the spectral indices against the SNRs age is modelled with a power-law function, providing an exponent $-0.07\pm0.03$ and amplitude $-0.49\pm0.02$ (normalised at 10 kyr), which are conservative with respect to previous studies of our Galaxy and the Large Magellanic Cloud.Comment: 33 pages, 15 figure, 15 tables. Submitted to MNRAS. QUIJOTE data maps available at https://research.iac.es/proyecto/quijot

L'approche bayésienne de l'analyse du fond diffus cosmologique

The main topic of this thesis is the analysis of Cosmic Microwave Background (CMB) data. In particular, I present a method, Bayesian Independent component analysis (BICA), that performs both CMB component separation and CMB power spectrum inference.I begin by presenting the basics of our understanding of the CMB emission and highlight the need for a robust error modelling at the map level. Then I present the main source of errors in the CMB products, namely the foregrounds.Component separation is a crucial and delicate step in CMB data analysis. I review several methods aiming at cleaning the CMB from foregroundsThen I present BICA. The method is formulated in a blind Bayesian framework. The posterior distribution provides an inference of the CMB map and power spectrum from the observation maps. Thus, the errors on the reconstruction include the uncertainties due the presence of foregrounds in the data. By considering particular choices of prior and sampling scheme, I show how the Bayesian formulation of component separation provide a unifying framework of which previous methods are special cases.I present the results of BICA when applied on both simulated data and 2013 Planck data. This method is able to reconstruct the CMB map and power spectrum on a large fraction of the sky. The main contributions of this thesis is to provide: 1) a CMB power spectrum on a large multipole range whose errors take the presence of foregrounds into account but without assuming physical models, 2) a CMB map inference together with an error model including both noise and foregrounds residuals.Le thème principal de cette thèse est l'analyse de données du fond diffus cosmologique (CMB). En particulier, je présente une méthode, Bayesian Independent Analysis (BICA), qui effectue à la fois la séparation des composants et l'inférence du spectre de puissance du CMB. Je commence par présenter les principes de base du CMB et souligne la nécessité d'une modélisation robuste des erreurs au niveau de la carte. Puis je présente la principale source d'erreurs dans les produits du CMB, à savoir les avant-plans. La séparation des composants est une étape cruciale dans l'analyse de données de CMB. Je passe en revue plusieurs méthodes visant à nettoyer le CMB des avant-plans. Puis je présente BICA. La méthode est formulée dans le cadre bayésien aveugle. Il en résulte une inférence jointe de la carte de CMB et de son spectre de puissance. Ainsi, les erreurs sur la reconstruction comprennent les incertitudes dues à la présence d'avant-plans dans les données. En considérant des choix particuliers de prior et d'échantillonnage, je montre comment la formulation bayésienne de séparation des composantes fournit un cadre unificateur dont les méthodes précédentes sont des cas particuliers. Je présente les résultats de BICA lorsqu'elle est appliquée sur des données simulées et les données Planck. Cette méthode est capable de reconstruire la carte du CMB et son spectre sur une large fraction du ciel. Les principales contributions de cette thèse sont : 1) un spectre de puissance du CMB dont les erreurs prennent en compte la présence d'avant-plans mais sans modèle physique, 2) une carte CMB avec un modèle d'erreur incluant à la fois le bruit et avant-plans

Semi-blind Bayesian inference of CMB map and power spectrum

We present a new blind formulation of the cosmic microwave background (CMB) inference problem. The approach relies on a phenomenological model of the multifrequency microwave sky without the need for physical models of the individual components. For all-sky and high resolution data, it unifies parts of the analysis that had previously been treated separately such as component separation and power spectrum inference. We describe an efficient sampling scheme that fully explores the component separation uncertainties on the inferred CMB products such as maps and/or power spectra. External information about individual components can be incorporated as a prior giving a flexible way to progressively and continuously introduce physical component separation from a maximally blind approach. We connect our Bayesian formalism to existing approaches such as Commander, spectral mismatch independent component analysis (SMICA), and internal linear combination (ILC), and discuss possible future extensions

PICO: Probe of Inflation and Cosmic Origins

The Probe of Inflation and Cosmic Origins (PICO) is a proposed probe-scale space mission consisting of an imaging polarimeter operating in frequency bands between 20 and 800 GHz. We describe the science achievable by PICO, which has sensitivity equivalent to more than 3300 Planck missions, the technical implementation, the schedule and cost

PICO: Probe of Inflation and Cosmic Origins

The Probe of Inflation and Cosmic Origins (PICO) is an imaging polarimeter that will scan the sky for 5 years in 21 frequency bands spread between 21 and 799 GHz. It will produce full-sky surveys of intensity and polarization with a final combined-map noise level of 0.87 $\mu$K arcmin for the required specifications, equivalent to 3300 Planck missions, and with our current best-estimate would have a noise level of 0.61 $\mu$K arcmin (6400 Planck missions). PICO will either determine the energy scale of inflation by detecting the tensor to scalar ratio at a level $r=5\times 10^{-4}~(5\sigma)$, or will rule out with more than $5\sigma$ all inflation models for which the characteristic scale in the potential is the Planck scale. With LSST's data it could rule out all models of slow-roll inflation. PICO will detect the sum of neutrino masses at $>4\sigma$, constrain the effective number of light particle species with $\Delta N_{\rm eff}<0.06~(2\sigma)$, and elucidate processes affecting the evolution of cosmic structures by measuring the optical depth to reionization with errors limited by cosmic variance and by constraining the evolution of the amplitude of linear fluctuations $\sigma_{8}(z)$ with sub-percent accuracy. Cross-correlating PICO's map of the thermal Sunyaev-Zeldovich effect with LSST's gold sample of galaxies will precisely trace the evolution of thermal pressure with $z$. PICO's maps of the Milky Way will be used to determine the make up of galactic dust and the role of magnetic fields in star formation efficiency. With 21 full sky legacy maps in intensity and polarization, which cannot be obtained in any other way, the mission will enrich many areas of astrophysics. PICO is the only single-platform instrument with the combination of sensitivity, angular resolution, frequency bands, and control of systematic effects that can deliver this compelling, timely, and broad science

PICO: Probe of Inflation and Cosmic Origins

The Probe of Inflation and Cosmic Origins (PICO) is an imaging polarimeter that will scan the sky for 5 years in 21 frequency bands spread between 21 and 799 GHz. It will produce full-sky surveys of intensity and polarization with a final combined-map noise level of 0.87 $\mu$K arcmin for the required specifications, equivalent to 3300 Planck missions, and with our current best-estimate would have a noise level of 0.61 $\mu$K arcmin (6400 Planck missions). PICO will either determine the energy scale of inflation by detecting the tensor to scalar ratio at a level $r=5\times 10^{-4}~(5\sigma)$, or will rule out with more than $5\sigma$ all inflation models for which the characteristic scale in the potential is the Planck scale. With LSST's data it could rule out all models of slow-roll inflation. PICO will detect the sum of neutrino masses at $>4\sigma$, constrain the effective number of light particle species with $\Delta N_{\rm eff}<0.06~(2\sigma)$, and elucidate processes affecting the evolution of cosmic structures by measuring the optical depth to reionization with errors limited by cosmic variance and by constraining the evolution of the amplitude of linear fluctuations $\sigma_{8}(z)$ with sub-percent accuracy. Cross-correlating PICO's map of the thermal Sunyaev-Zeldovich effect with LSST's gold sample of galaxies will precisely trace the evolution of thermal pressure with $z$. PICO's maps of the Milky Way will be used to determine the make up of galactic dust and the role of magnetic fields in star formation efficiency. With 21 full sky legacy maps in intensity and polarization, which cannot be obtained in any other way, the mission will enrich many areas of astrophysics. PICO is the only single-platform instrument with the combination of sensitivity, angular resolution, frequency bands, and control of systematic effects that can deliver this compelling, timely, and broad science