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Le rayonnement du Fond Diffus Cosmologique est une source riche et propre d’informations cosmologiques. L’étude du CMB au cours des dernières décennies a conduit à la mise en place d’un modèle standard pour la cosmologie et a permis de mesurer précisément ses principaux paramètres. Il a également transformé le domaine, en le basant davantage sur les données observationnelles et les approches numériques et statistiques.A l’heure actuelle, l’inflation est le principal paradigme décrivant les premiers moments de notre Univers. Elle prédit la génération de fluctuations de la densité de matière primordiale et des ondes gravitationnelles. Le signal de polarisation du CMB porte la signature de ces ondes gravitationnelles sous la forme de modes-B primordiaux. Une future génération de missions spatiale d’observation de la polarisation du CMB est bien adaptée à l’observation de cette signature de l’inflation.Cette thèse se concentre sur l’optimisation d’une future mission spatiale CMB qui observera le signal en modes-B pour atteindre une sensibilité de r = 0,001. Plus précisément, j’étudie la stratégie d’observation et l’impact des effets systématiques sur la qualité de la mesure de polarisation / The Cosmic Microwave Background radiation is a rich and clean source of Cosmological information. Study of the CMB over the past few decades has led to the establishment of a “Standard Model” for Cosmology and constrained many of its principal parameters. It hasalso transformed the field into a highly data-driven domain.Currently, Inflation is the leading paradigm describing the earliest moments of our Universe. It predicts the generation of primordial matter density fluctuations and gravitational waves. The CMB polarisation carries the signature of these gravitational waves in the form of primordial “B-modes”. A future generation of CMB polarisation space mission is well suited to observe this signature of Inflation.This thesis focuses on optimising a future CMB space mission that will observe the B-modesignal for reaching a sensitivity of r = 0.001. Specifically, I study the optimisation of the scanning strategy and the impact of systematics on the quality of polarisation measurement

Les particules fines (PM2.5) suscitent de plus en plus l’intérêt des pouvoirs publics en raison de leurs effets néfastes sur la qualité de l’air et la santé humaine. La mise en place des politiques de réductions efficaces des émissions requière une connaissance détaillée des contributions des principales sources aux concentrations ambiantes en PM. Ainsi, cette thèse a pour objectifs de caractériser la composition chimique des PM2.5, et de quantifier leurs sources d’émissions à Marseille. Pour se faire, une campagne de mesure d’un an (2011-2012) a été conduite sur le site de fond urbain de « Cinq avenues ». La spéciation chimique complète des filtres collectés a été réalisée, et 3 modèles-récepteurs ont été utilisés: CMB (Chemical Mass Balance), PMF (Positive Matrix Factorization), et ME-2 (Multilinear Engine). Bien que basés sur des concepts sensiblement différents, l’exercice d’intercomparaison des sorties de ces modèles a montré globalement un bon accord pour l’estimation des contributions de la combustion de biomasse (entre 23 et 33% en moyenne annuelle) et du trafic véhiculaire (14-26%). En revanche, des différences significatives sont observées pour la source industrielle (1-18%) et le sulfate d’ammonium (12-30%). Cette étude a mis en évidence une contribution importante de la matière organique (OM) qui représente en moyenne 42% des PM2.5. Quant à la quantification des sources, l’un des résultats marquants est la mise en évidence de deux types d’aérosols de combustion de biomasse, dont l’un provient très probablement du brûlage à l’air libre de déchets verts. Ce dernier peut même être considéré comme un contributeur majeur des PM2.5 à l’automne et en début d’hiver. / Fine particulate matter (PM2.5) has received considerable attention due to its impact on human health and air quality. Therefore, effective plans for human health protection require a detailed knowledge of the most relevant PM emission sources and their contributions to the ambient PM levels. Thus, this thesis aims to characterize the chemical composition of PM2.5 collected in Marseille area, and quantify the impacts of the main emission sources. To meet these objectives, a one-year monitoring campaign was conducted at the urban background site of “Cinq avenues” during the period of 2011-2012. A detailed chemical characterization of the collected PM2.5 filters was performed, and 3 receptor models were applied to this database: CMB (Chemical Mass Balance), PMF (Positive Matrix Factorization), and ME-2 (Multilinear Engine). Although based on significantly different concepts, the intercomparison exercise of the output data of the used models has generally showed a good agreement in estimating the source contributions of biomass burning (representing between 23 and 33% on annual average) and vehicular traffic (between 14 and 26%). In contrast, significant differences were observed for the industrial (1-18%) and ammonium sulfate (12-30%) sources. This study highlighted the significant contribution of organic matter (OM), which represents 42% of the PM2.5 mass, on average. Regarding the source apportionment results, one of the most striking findings is the identification of two types of biomass burning aerosol, one of which probably comes from open burning of green waste. The latter can even be considered a major contributor to the PM2.5 mass during fall and early winter

Nous vivons à une époque marquée par une abondance de données cosmologiques de haute résolution. Cet afflux de données engendré par les missions d'observation de nouvelle génération au sol et dans l'espace porte le potentiel de remodeler fondamentalement notre compréhension de l'univers et de ses principes physiques sous-jacents. Cependant, la complexité grande des données observées pose des défis aux approches conventionnelles d'analyse de données, soit en raison de coûts de calcul irréalisables, soit en raison des hypothèses simplificatrices utilisées dans ces algorithmes qui deviennent inadéquates dans des contextes haute résolution à faible bruit, conduisant à des résultats sous-optimaux. En réponse, la communauté scientifique s'est tournée vers des méthodes innovantes d'analyse de données, notamment les techniques d'apprentissage automatique (ML). Les modèles de ML, lorsqu'ils sont bien entraînés, peuvent identifier de manière autonome des correlations significatives dans les données de manière plus efficace et sans hypothèses restrictives inutiles. Bien que les méthodes de ML aient montré des promesses en astrophysique, elles présentent également des problèmes tels que le manque d'interprétabilité, les biais cachés et les estimations d'incertitude non calibrées, ce qui, jusqu'a maintenant, a entrave leur application dans d'importantes découvertes scientifiques. Ce projet s'inscrit dans le cadre de la collaboration "Learning the Universe" (LtU), axée sur la reconstruction des conditions initiales de l'univers, en utilisant une approche de modélisation bayésienne et en exploitant la puissance du ML. L'objectif de ce projet est de développer un cadre pour mener une inférence bayésienne au niveau des pixels dans des problèmes multidimensionnels. Dans cette thèse, je présente le développement d'un cadre d'apprentissage profond pour un échantillonnage rapide des postérieurs en dimensions élevées. Ce cadre utilise l'architecture "Hierarchical Probabilistic U-Net", qui combine la puissance de l'architecture U-Net dans l'apprentissage de cartes multidimensionnelles avec le rigoureux cadre d'inférence des autoencodeurs variationnels conditionnels. Notre modèle peut quantifier les incertitudes dans ses données d'entraînement et générer des échantillons à partir de la distribution a posteriori des paramètres, pouvant être utilisés pour dériver des estimations d'incertitude pour les paramètres inférés. L'efficacité de notre cadre est démontrée en l'appliquant au problème de la reconstruction de cartes du fond diffus cosmologique (CMB) pour en retirer de l'effet de lentille gravitationnelle faible. Notre travail constitue un atout essentiel pour effectuer une inférence de vraisemblance implicite en dimensions élevées dans les domaines astrophysiques. Il permet d'exploiter pleinement le potentiel des missions d'observation de nouvelle génération pour améliorer notre compréhension de l'univers et de ses lois physiques fondamentales. / We live in an era marked by an abundance of high-resolution cosmological data. This influx of data brought about by next-generation observational missions on the ground and in space, bears the potential of fundamentally reshaping our understanding of the universe and its underlying physical principles. However, the elevated complexity of the observed data poses challenges to conventional data analysis approaches, either due to infeasible computational costs or the simplifying assumptions used in these algorithms that become inadequate in high-resolution, low-noise contexts, leading to suboptimal results. In response, the scientific community has turned to innovative data analysis methods, including machine learning (ML) techniques. ML models, when well-trained, can autonomously identify meaningful patterns in data more efficiently and without unnecessary restrictive assumptions. Although ML methods have shown promise in astrophysics, they also exhibit issues like lack of interpretability, hidden biases, and uncalibrated uncertainty estimates, which have hindered their application in significant scientific discoveries. This project is defined within the context of the Learning the Universe (LtU) collaboration, focused on reconstructing the initial conditions of the universe, utilizing a Bayesian forward modeling approach and harnessing the power of ML. The goal of this project is to develop a framework for conducting Bayesian inference at the pixel level in high-dimensional problems. In this thesis, I present the development of a deep learning framework for fast high-dimensional posterior sampling. This framework utilizes the Hierarchical Probabilistic U-Net architecture, which combines the power of the U-Net architecture in learning high-dimensional mappings with the rigorous inference framework of Conditional Variational Autoencoders. Our model can quantify uncertainties in its training data and generate samples from the posterior distribution of parameters, which can be used to derive uncertainty estimates for the inferred parameters. The effectiveness of our framework is demonstrated by applying it to the problem of removing the weak gravitational lensing effect from the CMB. Our work stands as an essential asset to performing high-dimensional implicit likelihood inference in astrophysical domains. It enables utilizing the full potential of next-generation observational missions to improve our understanding of the universe and its fundamental physical laws.

L'expérience embarquée en ballon Archeops a cartographié les anisotropies du Fond Diffus Cosmologique (CMB) avec une résolution angulaire de 10 minutes d'arc sur une portion du ciel de 30%. Elle constitue également un banc-test pour la mission Planck-HFI de l'ESA.<br> Le travail de cette thèse a porté sur l'étalonnage sol à l'aide d'une source thermique millimétrique des trois vols effectués par Archeops et l'analyse des données obtenues. Le traitement des données ordonnées en temps est détaillé en plusieurs étapes : signalisation des données corrompues, soustraction des effets systématiques à basse fréquence, puis à haute fréquence. Dans ce dernier cas, une méthode originale est proposée qui permet à quelques détecteurs de satisfaire aux hypothèses de stationnarité et de gaussianité des codes de cartographie. Les solutions fournies par trois de ces codes sont comparés à l'aide de simulations.<br> Le spectre de puissance angulaire des anisotropies permet de contraindre les différents paramètres du modèle cosmologiquc standard, et par conséquent les paramètres physiques du modèle inflationnaire considéré pour générer les pertubations primordiales dont les anisotropies sont l'empreinte. Cette thèse présente ce qu'Archeops peut apporter dans ce domaine.

In recent years, the celebration activities of various types of folk-custom festivals in Taiwan have already been getting more and more attention from civilians. Festivities throughout the whole island are traditionally accompanied by loud and brightly colored firework displays. Among these activities, the firework display during the Chinese Lantern Festival in Kaohsiung City is one of the largest festivals in Taiwan every year. Therefore, it is important to investigate the influences of firework displays on ambient air quality during the Chinese Lantern Festival in Kaohsiung City. Field measurement of ambient gaseous pollutants and particulate matter (PM) was conducted on February 9-12, 2009, the Chinese Lantern Festival, in Kaohsiung City. Moreover, three kinds of firework powders obtained from the same factory producing Kaohsiung Lantern Festival fireworks were burned in a combustion chamber to determine the physicochemical properties of firework aerosols. Metallic elements were analyzed with an inductively coupled plasma-atomic emission spectrometer (ICP-AES). Ionic species and carbonaceous contents in the PM samples were analyzed with an ion chromatography (IC) and an elemental analyzer (EA), respectively. Finally, the source identification and apportionment of PM were analyzed by principal component analysis (PCA), enrichment factor (EF), and receptor modeling (CMB). For inorganic gaseous pollutants, the concentration peaks of NO, NO2, O3, CO were observed during the firework periods, and the concentration peak of NO was approximately 8.8 times higher than those during the non-firework periods. This study further revealed that, even at nighttime, ambient O3 could be reduced dramatically during the firework periods, whenas NO2 concentration increased concurrently, due to titration effects resulting from the prompt reaction of NO with O3 to form NO2 and O2. For organic gaseous pollutants, the concentration peak of toluene during the firework periods was approximately 2.2-4.1 times higher than those during the non-firework periods. Several metallic elements of PM during the firework display periods were obviously higher than those during the non-firework periods. On February 10, the concentrations of Mg, K, Pb, and Sr in PM2.5 were 10 times higher than those during the non-firework periods. Besides, the Cl-/Na+ ratio was slightly smaller than 1 in Kaohsiung Harbor, but it was approximately 3 during the firework display periods since Cl- came form chlorine content in firework aerosols at this time. Moreover, OC/EC ratio increased up to 2.8. In addition to the analysis of gaseous pollutants and PM during the Chinese Lantern Festival in Kaohsiung City, this study burned firework powders in a self-designed combustion chamber to measure the physicochemical properties of firework aerosols. In the results, K, Mg, Cl-, OC were major contents (<10%) in the aerosols produced from the burning firework powders. Moreover, Cl-/Na+ and OC/EC ratio were 15.0~23.4 and 2.9~3.2, respectively. Consequently, Cl-/Na+ and OC/EC ratio can be used as two important indicators of firework displays. Results obtained from PCA and CMB receptor modeling showed that the major sources of aerosols during the firework display periods were firework displays, motor/diesel vehicle exhanst, soil dusts, and marine aerosols. Besides, the firework displays on February 10 contributed approximately 25.2% and 16.6% of PM10 at two sampling sites, respectively.

In recent years, the rapid development of economy and industry in Xiamen Bay causes serious environmental problems, particularly poor air quality and visibility impairment. There are no large-scale industrial emission sources in Kinmen Island, however, its ambient air quality is always the poorest in Taiwan. Moreover, ambient air quality monitoring data showed that PM10 concentrations varied in daytime and at nighttime. Consequently, this study tired to ascertain the potential causes for this phenomenon. This study selected ten particulate matter (PM) sampling sites at Xiamen Bay, including five sites at Kinmen Island and five sites at metro Xiamen. Particulate matter sampling was conducted in daytime (8:00-17:00) and at nighttime (17:00-8:00), which included regular and intensive sampling. Regular sampling was conducted to collect PM10 with high-volume samplers three times a month from April 2009 to April 2010, while intensive sampling was conducted to collect fine (PM2.5) and coarse (PM2.5-10) particles with dichotomous samplers and particle size distribution with a MOUDI at site B2 for consecutive 5 days in the spring and winter of 2009~2010. After sampling, the physicochemical properties of PM, including mass concentrations, particle size distribution, water- soluble ionic species, metallic elements, and carbonaceous contents were further analyzed. The level of atmospheric PM is affected by meteorological condition, thus PM10 concentrations in winter and fall was much higher than those in spring and summer. Results from backward trajectories showed that the concentrations of PM10 blown from the north were generally higher than those from the south. Furthermore, t-test analysis indicated that PM10 concentrations in daytime and at nighttime at site B3 were significantly different (p-value<0.05). During the intensive sampling periods, PM10 concentrations were mainly affected by coarse particles compared to fine particles. The highest concentration for fine and coarse particle modes occurred at the size ranges of 0.32~0.56 £gm and 3.2~5.6 £gm, respectively. The most abundant water-soluble ionic species of PM10 were secondary inorganic aerosols (SO42-, NO3-, and NH4+) which accounted for 85% of total ions. The daytime and nighttime PM10 concentration ratios (D/N) for Mg, K, Ca, Cr, Mn, Fe, Zn, Al, Cu, As, and V were in the same order of magnitude, however, the D/N ratios of Cd, Pb, Ni, and Ti in spring and summer varied higher than an order of magnitude, indicating that the emission sources of PM were different in daytime and at nighttime. Correlation analysis of OC and EC showed that OC and EC at nighttime had a higher correlation than those in daytime, while OC and EC had a higher correlation in Kinmen Island than those in metro Xiamen, indicating carbonaceous sources must be different in summer and winter at Xiamen Bay. Enrichment factor analysis revealed that ceramic industry, stone processing, and cement industry had higher correlation with PM10 concentration than utility power plants. Crustal dusts consisted of road dusts, farmland dusts, and constructive dusts, while biomass burning was not a negligible sources. Results obtained from PCA and CMB receptor modeling showed that major sources of PM in Xiamen Bay were secondary inorganic aerosols, fuel and biomass burning, marine aerosols, vehicular exhansts, and soil dusts. Besides, stone processing, cement industry, ceramic industry, and utility power plants had the highest contribution in winter. Their contributions in daytime and at nighttime were 38% and 45%, respectively.