Date of Award


Document Type

Open Access Dissertation

Degree Name

Doctor of Philosophy in Applied Physics (PhD)

Administrative Home Department

Department of Physics

Advisor 1

Brian Fick

Committee Member 1

David Nitz

Committee Member 2

Petra Huentemeyer

Committee Member 3

Simon Carn


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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Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License.