Graph Neural Network-Based Optimization of IceCube-Gen2 Geometry

IceCube-Gen2 is the planned upgrade of the IceCube Neutrino Observatory at the South Pole designed to probe the high-energy neutrino sky from TeV to EeV energies, with a ten times more volume than the current IceCube detector. As more strings will be included at a larger separation distance, we need a geometry that provides us with an optimal ability in capturing the events and recording their information despite the increased spacing between strings. In this study, we utilize graph neural networks as the reconstruction method for IceCube-Gen2, and evaluate the performance of this algorithm under 16 proposed candidate geometries, arranging a prototype detector with 196 strings in 4 different shapes and 4 different geometric areas.