Along their long propagation from production to detection, neutrinos undergo flavour conversions that convert their types or flavours1,2. High-energy astrophysical neutrinos propagate unperturbed over a billion light years in vacuum3 and are sensitive to small effects caused by new physics. Effects of quantum gravity4 are expected to appear at the Planck energy scale. Such a high-energy universe would have existed only immediately after the Big Bang and is inaccessible by human technologies. On the other hand, quantum gravity effects may exist in our low-energy vacuum5–8, but are suppressed by inverse powers of the Planck energy. Measuring the coupling of particles to such small effects is difficult via kinematic observables, but could be observable through flavour conversions. Here we report a search with the IceCube Neutrino Observatory, using astrophysical neutrino flavours9,10 to search for new space–time structure. We did not find any evidence of anomalous flavour conversion in the IceCube astrophysical neutrino flavour data. We apply the most stringent limits of any known technologies, down to 10−42 GeV−2 with Bayes factor greater than 10 on the dimension-six operators that parameterize the space–time defects. We thus unambiguously reach the parameter space of quantum-gravity-motivated physics.

Atmospheric muon neutrinos are produced by meson decays in cosmic-ray-induced air showers. The flux depends on meteorological quantities such as the air temperature, which affects the density of air. Competition between decay and re-interaction of those mesons in the first particle production generations gives rise to a higher neutrino flux when the air density in the stratosphere is lower, corresponding to a higher temperature. A measurement of a temperature dependence of the atmospheric νμ flux provides a novel method for constraining hadronic interaction models of air showers. It is particularly sensitive to the production of kaons. Studying this temperature dependence for the first time requires a large sample of high-energy neutrinos as well as a detailed understanding of atmospheric properties. We report the significant (>10σ) observation of a correlation between the rate of more than 260,000 neutrinos, detected by IceCube between 2012 and 2018, and atmospheric temperatures of the stratosphere, measured by the Atmospheric Infrared Sounder (AIRS) instrument aboard NASA’s AQUA satellite. For the observed 10 % seasonal change of effective atmospheric temperature we measure a 3.5(3) % change in the muon neutrino flux. This observed correlation deviates by about 2-3 standard deviations from the expected correlation of 4.3 % as obtained from theoretical predictions under the assumption of various hadronic interaction models.

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 2.8σ significance.

Name that Neutrino is a citizen science project where volunteers aid in classification of events for the IceCube Neutrino Observatory, an immense particle detector at the geographic South Pole. From March 2023 to September 2023, volunteers did classifications of videos produced from simulated data of both neutrino signal and background interactions. Name that Neutrino obtained more than 128,000 classifications by over 1800 registered volunteers that were compared to results obtained by a deep neural network machine-learning algorithm. Possible improvements for both Name that Neutrino and the deep neural network are discussed.