Matière noire

Résumé

vignette|redresse=1.1|Répartition de la densité d'énergie de l'Univers après exploitation des premières données du satellite Planck. La matière noire en est une des composantes principales. La matière noire ou matière sombre, est une catégorie de matière hypothétique, invoquée dans le cadre du Modèle ΛCDM pour rendre compte de certaines observations astrophysiques, notamment les estimations de la masse des galaxies ou des amas de galaxies et les propriétés des fluctuations du fond diffus cosmologique. La matière noire n'interagit pas, ou extrêmement peu, avec la matière baryonique (matière "ordinaire"), ni avec les photons (ondes radio et lumière), rendant sa détection et sa caractérisation très difficiles. Sa présence n'est détectée que par son influence gravitationnelle, non négligeable et importante dans divers modèles cosmologiques et astrophysiques. En ce qui concerne la composition de la matière noire, différentes hypothèses sont explorées : gaz moléculaire, étoiles mortes, n

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Cosmology is the study of the structure and evolution of the universe as a whole. This course describes the principal themes of cosmology, as seen from the point of view of observations.

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Non topological solitons, Q balls can arise in many particle theories with U(1) global symmetries. As was shown by Cohen et al. [2], if the corresponding scalar field couples to massless fermions, large Q-balls are unstable and evaporate, producing a fermion flux proportional to the Q ball's surface. In this work we analyse Q-ball instabilities as a function of Q-ball size and fermion mass. In particular, we construct an exact quantum-mechanical description of the evaporating Q-ball. This new construction provides an alternative method to compute Q-Ball's evaporation rates. We shall also find a new expression for the upper bound on evaporation as a function of the produced fermion mass and study the effects of the size of the Q ball on particle production. We also analyse what happens if external fermion is scattered on a Q ball and demonstrate that it can be converted into antiparticle with a probability of the order of one. This result has important implications for astrophysical applications of dark matter Q balls.

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Methods for strong gravitational lens detection and analysis using machine learning and high performance computing

Christoph Ernst René Schäfer

The study of strong gravitational lenses is a relatively new scientific field in astronomy with many applications in cosmology. Its unique observables allow astronomers to trace dark matter, determine the expansion of the universe and study galaxy evolution. While strong lenses are relatively rare phenomena, recent and future improvements in observational instrumentation are expected to lead to a sharp increase in the number and quality of their observation. With that increase comes however an observational challenge from the sheer quantity of data the lenses are hidden in. Current classification and modelling techniques are simply not capable of handling the new data in a reasonable timeframe. As a consequence this thesis concerns itself with the development and improvement of numerical methods necessary to find and analyse these newly found lenses, borrowing from deep learning and high performance computing techniques. Deep learning, a field of machine learning, shows significant success in finding lenses in simulations. Performing significantly better than any other classification method, CNN-based lens finders reach almost the required classification efficiency and accuracy for a fully automated Euclid strong lensing pipeline. Its dependency on a varied and complete training set and the difficulty creating it make its immediate application to new surveys however more difficult. Incorporating high performance computing techniques and more concurrent algorithm design into lens analysis results in crucial speed-ups for strong lens analysis. Applied to Lenstool, a popular mass modelling tool for gravitational lenses, the resulting software can be run on modern supercomputers, using Graphics Processing Units (GPU) and CPUs simultaneously. Running it on state-of-the-art hardware makes it possible to reduce computation time to an acceptable level when mass modelling complex cluster lenses.

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Physics beyond the Standard Model can appear as new particles too heavy to be produced in experiments (the energy frontier) or interacting too weakly to be seen in our measurements (the intensity frontier). In the first part of this thesis, we study two dark matter models of electroweak WIMP coming from two limiting cases in the MSSM: the Higgsino and the Wino model. The dark matter candidate is a particle too heavy to be directly seen in colliders but it could be seen indirectly by astrophysical observations. Dark matter particles in the galaxy can annihilate to Standard Model particles and be detected by satellites. We consider the charged neutral mass splitting as a free parameter in the theory and investigate its effect on the Sommerfeld enhancement which gives an important boost to the annihilation cross-section, the main observable of indirect detection. In the second part of this thesis, we present an idea of a fixed target experiment so search for dark sector models, a class of model with very weakly interacting particles. This experiment uses the experimental setup needed for a future muon collider but is independent from the muon collider program. We study the reach of our experiment for three benchmark models, the dark photon, the dark Higgs, the heavy neutral lepton and we compare its expected performances to current and future experiments. In the third part of this thesis, we consider a more model-independent approach to the search for new physics. If new states are heavy, they can be integrated out and their leading effects are encoded in effective operators made of Standard Model fields. Assuming baryon and lepton number conservation, one can classify the effective operators of dimension-6 which give the leading contribution and constrain the coefficient of these operators by looking at precision observables. We compile results from LEP-I and LEP-II experiments as well as neutrino scattering and other low-energy observables. We allow all operators to be present with an arbitrary flavour structure. Our result can then be used to translate these constaints to specific models of new physics.

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Methods for strong gravitational lens detection and analysis using machine learning and high performance computing

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