Dark matter

Summary

Dark matter is a hypothetical form of matter thought to account for approximately 85% of the matter in the universe. Dark matter is called "dark" because it does not appear to interact with the electromagnetic field, which means it does not absorb, reflect, or emit electromagnetic radiation and is, therefore, difficult to detect. Various astrophysical observations – including gravitational effects which cannot be explained by currently accepted theories of gravity unless more matter is present than can be seen – imply dark matter's presence. For this reason, most experts think that dark matter is abundant in the universe and has had a strong influence on its structure and evolution. The primary evidence for dark matter comes from calculations showing that many galaxies would behave quite differently if they did not contain a large amount of unseen matter. Some galaxies would not have formed at all and others would not move as they currently do. Other lines of ev

Official source

https://en.wikipedia.org/wiki/Dark_matter

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EPFL2006

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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