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Explorer l'aube cosmique et l'époque de réionisation avec le signal 21 cm

Abstract : Simulations are increasingly able to capture the intricacies of the Epoch of Reionization, during which the neutral hydrogen in the Universe was ionized by the first luminous sources. Databases encompassing the range of possible signals will be needed to constrain parameter values when 21~cm observations are available. In preparation for upcoming experiments such as the SKA, we have developed a database of high-resolution EoR lightcones (21ssd.obspm.fr), along with realistic thermal noise modelling. We examine frameworks with which we can quantify the difference between entries in this database, specifically with the power spectrum and pixel distribution function. We find that the two diagnostics are sensitive to different parameters, meaning they can be used together to extract maximumal information. Then, using the 21cmFAST code, we explore how to optimally sample a parameter space (so that it is more homogeneous and isotropic), in order to provide the best set-up for parameter extraction. Finally, the improved sampling is used in training a neural network. The neural network uses observables as input data, and attempts to estimate the corresponding parameter values. When the optimal sampling is used as training data, we find that the neural network is able to estimate parameter values with a modest improvement in accuracy.
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  • HAL Id : tel-02107695, version 1

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Evan Eames. Explorer l'aube cosmique et l'époque de réionisation avec le signal 21 cm. Astrophysique [astro-ph]. Université Paris sciences et lettres, 2018. Français. ⟨NNT : 2018PSLEO008⟩. ⟨tel-02107695⟩

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