Presentation + Discussion
Abstract: My PhD project focuses on reconstructing the electron neutrino energy spectrum using IceCube data. A cascade Monte Carlo dataset used for the Galactic plane analysis, developed by Mirco Hünnefeld, serves as the test sample for setting up the analysis. Each neutrino cascade event contains numerous descriptive parameters, and machine learning techniques are employed to identify the parameters with the most significant informational value. Using decision tree algorithms, the analysis will unfold the energy spectrum. This work aims to produce an accurate electron neutrino energy spectrum, contributing to our understanding of neutrino physics and astrophysics.