Abstract: With the discovery of the astrophysical neutrino flux by the IceCube Neutrino Observatory in 2013, the foundation for neutrino astronomy was established. In subsequent years, the first point-like neutrino source candidates, a flaring blazar known as TXS 0506+056 and the active galaxy NGC 1068, emerged. Enabled by novel tools built upon deep learning, IceCube recently succeeded in a new milestone, providing strong evidence for the emission of high-energy neutrinos in the Galactic plane. The observed Galactic neutrino flux is believed to originate from diffuse interactions of cosmic rays, possibly in addition to contributions from unresolved point-like sources. The Galactic contribution may explain up to 10 % of the total astrophysical neutrino flux previously measured by IceCube. This observation opens a new window to study the high-energy universe, allowing us to further probe the properties of our own Galaxy and the origin of cosmic rays. In this contribution, I will highlight the recent observation by IceCube and the technical advances that made it possible, in addition to discussing future areas in which machine learning may play a pivotal role in establishing neutrino astronomy.
Pushing the boundaries of Neutrino Astronomy with Machine Learning
TU Dortmund University
Thursday, February 15, 2024