Vast and Fast Data in the era of the large astrophysics and particle physics experiments

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Abstract

Since the beginning of the third millennium, we have entered the Information Age, an era dominated by vast volumes of data and information generated by our modern societies. This era has critical implications for research, the progress of which now relies on large cutting-edge research experiments. The data collected by these experiments will be complex to handle, such that novel computational methods are needed to extract and analyze their information content. My research focuses on developing such methods in the context of upcoming large astrophysics and particle physics experiments. In this interdisciplinary thesis, I first present the implementation of a new computational tool building upon recent mathematical morphology techniques, the component trees, to efficiently analyze the connected structures observed in vast images and volumes. Then, I explore the astrophysics of the Epoch of Reionization, a key cosmic epoch in the history of the Universe, using UV spectroscopic and 21-cm observations. In particular, my work examines how future telescopes such as the James Webb Space Telescope and the Square Kilometer Array will help us to constrain the properties of the sources of reionization. Finally, I present a fast and efficient track reconstruction algorithm for the upcoming antiProton ANnihilation at DArmstadt (PANDA) particle physics experiment. PANDA will study collisions of protons (or nuclei) and antiprotons at very high interaction rates to explore the physics of strong interactions with unprecedented accuracy.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • University of Groningen
Supervisors/Advisors
  • Kalantar-Nayestanaki, Nasser, Supervisor
  • Koopmans, Léon, Supervisor
  • Wilkinson, Michael, Co-supervisor
  • Messchendorp, Johan, Co-supervisor
Award date1-Oct-2021
Place of Publication[Groningen]
Publisher
Print ISBNs978-94-6423-410-7
DOIs
Publication statusPublished - 2021

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