Thesis defense of Karolin Hymon

Besides the detection of astrophysical neutrinos, atmospheric neutrinos from cosmicray-induced air showers are detected at unprecedented statistics with the IceCube Neutrino Observatory. The conventional component of the atmospheric neutrino flux is produced in decays of kaons and pions. Due to seasonal changes in the atmospheric temperature, the neutrino flux undergoes a seasonal variation. When the temperature increases, the atmosphere expands, and more neutrinos are expected to be produced. Additionally, the seasonal variation increases with energy, as parent particles interact at higher altitudes in the atmosphere, where seasonal  temperature variations are larger. The interaction cross section increases with energy and the probability for the parent meson to decay increases. The investigation of seasonal variations serves as an accurate background determination in the search for astrophysical neutrinos and the study of hadronic interactions in atmospheric particle cascades. In this thesis, seasonal variations in the atmospheric neutrino flux are measured energydependently for the first time based on 11.5 years of IceCube data. The determination of the neutrino energy presents an ill-conditioned inverse problem, requiring to infer the energy from measured detector quantities. This challenge is addressed by the Dortmund Spectrum Estimation Algorithm (DSEA+), which utilizes machine learning methods to unfold the neutrino energy. The determined variation strength is compared to theoretical predictions from MCEq, and in particular to the calculation with the atmospheric model NRLMSISE-00.

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