Abstract
Anomalous extensive air showers have yet to be detected by cosmic ray observatories. Fluorescence detectors provide a way to view the air showers created by cosmic rays with primary energies reaching up to hundreds of EeV . The resulting air showers produced by these highly energetic collisions can contain features that deviate from average air showers. Detection of these anomalous events may provide information into unknown regions of particle physics, and place constraints on cross-sectional interaction lengths of protons. In this dissertation, I propose measurements of extensive air shower profiles that are used in a machine learning pipeline to distinguish a typical shower from an anomalous shower. Finally, constraints on yearly detection of anomalous events using the machine learning pipeline are given based on EPOS-LHCand QGSJET-II simulations for the Pierre Auger Observatory FD.
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