Vast and fast data in the era of large astrophysics and particle physics experiments

Since the beginning of the third millennium, we have entered the Information Age, an eradominated by vast volumes of data and information generated by our modern societies. This era hascritical implications for research, the progress of which now relies on large cutting-edge researchexperiments. The data collected by these experiments will be complex to handle, such that novelcomputational methods are needed to extract and analyze their information content.  My researchfocuses on developing such methods in the context of upcoming large astrophysics and particlephysics experiments. In this interdisciplinary thesis, I first present the implementation of a newcomputational tool building upon recent mathematical morphology techniques, the componenttrees, to efficiently analyze the connected structures observed in vast images and volumes. Then, Iexplore the astrophysics of the Epoch of Reionization, a key cosmic epoch in the history of theUniverse, using UV spectroscopic and 21-cm observations. In particular, my work examines howfuture telescopes such as the James Webb Space Telescope and the Square Kilometer Array will helpus to constrain the properties of the sources of reionization. Finally, I present a fast and efficienttrack reconstruction algorithm for the upcoming antiProton ANnihilation at DArmstadt (PANDA)particle physics experiment. PANDA will study collisions of protons (or nuclei) and antiprotons atvery high luminosities and interaction rates to explore the physics of strong interactions with unprecedentedaccuracy.

Dissertation: http://hdl.handle.net/11370/6cf93e50-5981-4a6a-bb9b-eb931442cc2a