Speaker
Description
The IceCube Neutrino Observatory is a neutrino detector located at the South Pole consisting of a three-dimensional array of optical sensors embedded deep within the Antarctic ice. It records the Cherenkov radiation produced by charged particles that are generated when neutrinos interact in the ice. The trajectory of the interacting neutrino can be inferred by analyzing the spatio-temporal distribution of the observed Cherenkov light. Muon tracks from high-energy, charged-current muon neutrino interactions are of particular interest for neutrino astronomy due to their sub-degree angular resolution. The current state-of-the-art reconstruction method, used to analyze IceCube's high-statistics samples of these events, assumes that Cherenkov light is emitted uniformly along the muon trajectory. It relies on knowing the corresponding photon arrival time distributions for each event and sensor combination. These PDFs were extracted from detector simulations using dated interpolation techniques that do not scale to the current complexity of the problem. Here we present a machine learning approach, using a mixture density network to extract new state-of-the-art photon arrival time distributions with an improved model for the properties of Antarctic ice. Building on the improved correctness of the PDFs and including information about the muon energy loss pattern, we devise methods to mitigate the impact of stochastic energy losses on the angular reconstruction. We compare the performance of the new methods to the current standard by leveraging a novel GPU-accelerated JAX implementation of the reconstruction method that greatly reduces the runtime of the algorithm.
