Chasing Hot Stuff in the Universe with Cool Stuff on Earth: Numerical and Deep Learning Methods for Dark Matter Annihilation
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Type
ThesisThesis type
Doctor of PhilosophyAuthor/s
List, FlorianAbstract
There is ample evidence for the existence of dark matter (DM) from cosmological observations. However, despite zealous efforts by the experimental particle physics community over the past decades, no confirmed detection of a DM particle has been reported to date, and its nature ...
See moreThere is ample evidence for the existence of dark matter (DM) from cosmological observations. However, despite zealous efforts by the experimental particle physics community over the past decades, no confirmed detection of a DM particle has been reported to date, and its nature hence remains a mystery. A central theme of this work is a particular type of interaction between DM and the visible Universe, whose detection (or non-detection) can potentially shed light onto the particle nature of DM, namely DM annihilation into standard model particles. We develop numerical and deep learning methods for different problems relating to DM annihilation, and we present and discuss the results obtained with our methods. Another subject matter is the cosmic age between Recombination and Reionisation, i.e. the Dark Ages and Cosmic Dawn, with a particular emphasis on the 21cm line from neutral hydrogen, whose detection by next-generation radio telescopes - most notably the Square Kilometer Array (SKA) - promises major discoveries in the coming years. First, we devise a new method for incorporating DM annihilation feedback (DMAF) into cosmological simulations, and we study the effect on the intergalactic medium and the 21cm signal before the Epoch of Reionisation. Then, we show that Generative Adversarial Networks (GANs) are powerful tools for the fast generation of realistic astrophysical mock data. Specifically, we use them to emulate the imprint of DMAF on the gas density field and to simulate 21cm tomography images as a function of different astrophysical parameters. Finally, we analyse the gamma-ray Galactic Centre Excess in the Fermi data, for which DM annihilation has been proposed as a possible explanation, harnessing deep learning techniques.
See less
See moreThere is ample evidence for the existence of dark matter (DM) from cosmological observations. However, despite zealous efforts by the experimental particle physics community over the past decades, no confirmed detection of a DM particle has been reported to date, and its nature hence remains a mystery. A central theme of this work is a particular type of interaction between DM and the visible Universe, whose detection (or non-detection) can potentially shed light onto the particle nature of DM, namely DM annihilation into standard model particles. We develop numerical and deep learning methods for different problems relating to DM annihilation, and we present and discuss the results obtained with our methods. Another subject matter is the cosmic age between Recombination and Reionisation, i.e. the Dark Ages and Cosmic Dawn, with a particular emphasis on the 21cm line from neutral hydrogen, whose detection by next-generation radio telescopes - most notably the Square Kilometer Array (SKA) - promises major discoveries in the coming years. First, we devise a new method for incorporating DM annihilation feedback (DMAF) into cosmological simulations, and we study the effect on the intergalactic medium and the 21cm signal before the Epoch of Reionisation. Then, we show that Generative Adversarial Networks (GANs) are powerful tools for the fast generation of realistic astrophysical mock data. Specifically, we use them to emulate the imprint of DMAF on the gas density field and to simulate 21cm tomography images as a function of different astrophysical parameters. Finally, we analyse the gamma-ray Galactic Centre Excess in the Fermi data, for which DM annihilation has been proposed as a possible explanation, harnessing deep learning techniques.
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Date
2021Rights statement
The author retains copyright of this thesis. It may only be used for the purposes of research and study. It must not be used for any other purposes and may not be transmitted or shared with others without prior permission.Faculty/School
Faculty of Science, School of PhysicsAwarding institution
The University of SydneyShare