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The origin of high-energy cosmic rays, atomic nuclei that continuously impact Earth’s atmosphere, is unknown. Because of deflection by interstellar magnetic fields, cosmic rays produced within the Milky Way arrive at Earth from random directions. However, cosmic rays interact with matter near their sources and during propagation, which produces high-energy neutrinos. We searched for neutrino emission using machine learning techniques applied to 10 years of data from the IceCube Neutrino Observatory. By comparing diffuse emission models to a background-only hypothesis, we identified neutrino emission from the Galactic plane at the 4.5σ level of significance. The signal is consistent with diffuse emission of neutrinos from the Milky Way but could also arise from a population of unresolved point sources.

 

High-energy tau neutrinos are rarely produced in atmospheric cosmic-ray showers or at cosmic particle accelerators, but are expected to emerge during neutrino propagation over cosmic distances due to flavor mixing. When high energy tau neutrinos interact inside the IceCube detector, two spatially separated energy depositions may be resolved, the first from the charged current interaction and the second from the tau lepton decay. We report a novel analysis of 7.5 years of IceCube data that identifies two candidate tau neutrinos among the 60 “High-Energy Starting Events” (HESE) collected during that period. The HESE sample offers high purity, all-sky sensitivity, and distinct observational signatures for each neutrino flavor, enabling a new measurement of the flavor composition. The measured astrophysical neutrino flavor composition is consistent with expectations, and an astrophysical tau neutrino flux is indicated at 2.8$$\sigma $$$\sigma$significance.

 

The arrival directions of astrophysical neutrinos indicate point source neutrino emission from NGC 1068. 

Abstract The reconstruction of event-level information, such as the direction or energy of a neutrino interacting in IceCube DeepCore, is a crucial ingredient to many physics analyses. Algorithms to extract this high level information from the detector’s raw data have been successfully developed and used for high energy events. In this work, we address unique challenges associated with the reconstruction of lower energy events in the range of a few to hundreds of GeV and present two separate, state-of-the-art algorithms. One algorithm focuses on the fast directional reconstruction of events based on unscattered light. The second algorithm is a likelihood-based multipurpose reconstruction offering superior resolutions, at the expense of larger computational cost.