A Machine Learning Approach for Comprehending Cosmic Expansion

Detta är en Master-uppsats från KTH/Fysik

Sammanfattning: This thesis aims at using novel machine learning techniques to test the dynamics of the Universe via the cosmological redshift-distance test. Currently, one of the most outstanding questions in cosmology is the physical cause of the accelerating cosmic expansion observed with supernovae. Simultaneously, tensions in measurements of the Hubble expansion parameter $H_0$ are emerging. Measuring the Universe expansion with next generation galaxy imaging surveys, such as provided by the Vera Rubin Observatory, offers the opportunity to discover new physics governing the Universe dynamics. In this thesis, with the long-term goal to unravel these peculiarities, we create a deep generative model in the form of a convolutional variational auto-encoder (VAE), trained with a "Variational Mixture of Posteriors" prior (VampPrior) and high-resolution galaxy images from the simulation project \texttt{TNG-50}. Our model is able to learn a prior on the visual features of galaxies and can generate synthetic galaxy images which preserve the coarse features (shape, size, inclination, and surface brightness profile), but not finer morphological features, such as spiral arms. The generative model for galaxy images is applicable to uses outside the scope of this thesis and is thus a contribution in itself. We next implement a cosmological pinhole camera model, taking angular diameter changes with redshift into account, to forward simulate the actual observation on a telescope detector. Building upon the hypothesis that certain features of galaxies should be of proper physical sizes, we use probabilistic triangulation to find the comoving distance $r(z,\Omega)$ to these in a flat ($K=0$) Universe. Using a sample of high-resolution galaxy images from redshifts $z\in[0.05,0.5]$ from \texttt{TNG-50}, we demonstrate that the implemented Bayesian inference approach successfully estimates $r(z)$ within $1\sigma$-error ($\Delta r_{\text{est}} = 140$ $(580)$ Mpc for $z=0.05$ $(0.5)$). Including the surface brightness attenuation and utilizing the avalanche of upcoming galaxy images could significantly lower the uncertainties. This thesis thus shows a promising path forward utilizing novel machine learning techniques and massive next-generation imaging data to improve and generalize the traditional cosmological angular-diameter test, which in turn has the potential to increase our understanding of the Universe.

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