A precise understanding of the optical properties of the instrumented Antarctic ice sheet is crucial to the performance of the IceCube Neutrino Observatory, a cubic-kilometer Cherenkov array of 5,160 digital optical modules (DOMs) deployed in the deep ice below the geographic South Pole. We present an update to the description of the ice tilt, which describes the undulation of layers of constant optical properties as a function of depth and transverse position in the detector. To date, tilt modeling has been based solely on stratigraphy measurements performed by a laser dust logger during the deployment of the array. We now show that it can independently be deduced using calibration data from LEDs located in the DOMs. The new fully volumetric tilt model not only confirms the magnitude of the tilt along the direction orthogonal to the ice flow obtained from prior dust logging, but also includes a newly discovered tilt component along the flow.

Ultra-high energy cosmic rays (UHE CRs) interacting with the cosmic radiation background produce two cosmogenic messengers: neutrinos with energies in the EeV range and gamma rays accumulating in the GeV-TeV range. The most optimistic scenario for cosmogenic fluxes assumes the dominance of protons above the Greisen-Zatsepin-Kuzmin threshold of resonant scattering with photons in the cosmic microwave background. Whereas these optimistic cosmogenic fluxes are testable with present observatories, the corresponding predictions of heavier UHE CR composition models are orders of magnitude smaller, falling within the domain of more sensitive future detectors. In this study we use the latest results of the Pierre Auger observatory for the UHE CR spectrum and chemical composition to derive conservative lower limits on the cosmogenic neutrino and gamma ray fluxes. We investigate the prospects and requirements of future large-scale neutrino and CR observatories to observe these fluxes.

The IceCube Neutrino Observatory is a cubic-kilometer high-energy neutrino detector deployed in the Antarctic ice. Two major event classes are charged-current electron and muon neutrino interactions. In this contribution, we discuss the inference of direction and energy for these classes using conditional normalizing flows. They allow to derive a posterior distribution for each individual event based on the raw data that can include systematic uncertainties, which makes them very promising for next-generation reconstructions.For each normalizing flow we use the differential entropy and the KL-divergence to its maximum entropy approximation to interpret the results. The normalizing flows correctly incorporate complex optical properties of the Antarctic ice and their relation to the embedded detector. For showers, the differential entropy increases in regions of high photon absorption and decreases in clear ice. For muons, the differential entropy strongly correlates with the contained track length. Coverage is maintained, even for low photon counts and highly asymmetrical contour shapes. For high-photon counts, the distributions get narrower and become more symmetrical, as expected from the asymptotic theorem of Bernstein-von-Mises. For shower directional reconstruction, we find the region between 1 TeV and 100 TeV to potentially benefit the most from normalizing flows because of azimuth-zenith asymmetries which have been neglected in previous analyses by assuming symmetrical contours. Events in this energy range play a vital role in the recent discovery of the galactic plane diffuse neutrino emission.

The initial evidence of astrophysical neutrinos by the IceCube Neutrino Observatory stemmed from the high-energy starting events (HESE) sample: a selection of the highest-energy neutrino interactions that occurred within the detector fiducial volume. Each event was reconstructed based on our best knowledge of the ice at the time, with the latest results published in a description of the sample using 7.5 years of data. Since then, several major improvements in ice modeling have occurred using in-situ calibration data. These include a microscopic description of ice anisotropy arising from ice crystal birefringence and a more complete mapping of the ice layer undulations across the detector. The improvements feed into more accurate descriptions of individual events, and can especially affect the directional reconstruction of particle showers. Here, we apply the latest ice model in an exact manner to reconstruct IceCube's high-energy events using DirectFit. This reconstruction samples posterior distributions across parameters of interest by performing full event resimulation and photon propagation at each step. We obtain improved per-event descriptions, as well as updates on previously published source searches using the aggregated sample.

IceCube DeepCore is an extension of the IceCube Neutrino Observatory designed to measure GeV scale atmospheric neutrino interactions for the purpose of neutrino oscillation studies. Distinguishing muon neutrinos from other flavors and reconstructing inelasticity are especially difficult tasks at GeV scale energies in IceCube DeepCore due to sparse instrumentation. Convolutional neural networks (CNNs) have been found to have better success at neutrino event reconstruction than conventional likelihood-based methods. In this contribution, we present a new CNN model that exploits time and depth translational symmetry in IceCube DeepCore data and present the model's performance, specifically for flavor identification and inelasticity reconstruction.