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Abstract The IceCube Neutrino Observatory, a cubic kilometer scale Cherenkov detector deployed in the deep ice at the geographic South Pole, investigates extreme astrophysical phenomena by studying the corresponding high-energy neutrino signal. Its discovery of a diffuse flux of astrophysical neutrinos with energies up to the PeV scale in 2013 has triggered a vast effort to identify the mostly unknown sources of these high energy neutrinos. Here, we present a new IceCube point-source search that improves the accuracy of the statistical analysis, especially at energies of a few TeV and below. The new approach is based on multidimensional kernel density estimation for the probability density functions and new estimators for the observables, namely the reconstructed energy and the estimated angular uncertainty on the reconstructed arrival direction. The more accurate analysis provides an improvement in discovery potential up to ∼30% over previous works for hard spectrum sources. 

Radio pulses generated by cosmic-ray air showers can be used to reconstruct key properties like the energy and depth of the electromagnetic component of cosmic-ray air showers. Radio detection threshold, influenced by natural and anthropogenic radio background, can be reduced through various techniques. In this work, we demonstrate that convolutional neural networks (CNNs) are an effective way to lower the threshold. We developed two CNNs: a classifier to distinguish radio signal waveforms from background noise and a denoiser to clean contaminated radio signals. Following the training and testing phases, we applied the networks to air-shower data triggered by scintillation detectors of the prototype station for the enhancement of IceTop, IceCube’s surface array at the South Pole. Over a four-month period, we identified 554 cosmic-ray events in coincidence with IceTop, approximately five times more compared to a reference method based on a cut on the signal-to-noise ratio. Comparisons with IceTop measurements of the same air showers confirmed that the CNNs reliably identified cosmic-ray radio pulses and outperformed the reference method. Additionally, we find that CNNs reduce the false-positive rate of air-shower candidates and effectively denoise radio waveforms, thereby improving the accuracy of the power and arrival time reconstruction of radio pulses. 

The hot nine-component system HD 93206, which contains a gravitationally bounded eclipsing Ac1+Ac2 binary ( P  = 5.9987 d) and a spectroscopic Aa1+Aa2 ( P  = 20.734 d) binary can provide important insights into the origin and evolution of massive stars. Using archival and new spectra, and a rich collection of ground-based and space photometric observations, we carried out a detailed study of this object. We provide a much improved description of both short orbits and a good estimate of the mutual period of both binaries of about 14 500 d (i.e. 40 years). For the first time, we detected weak lines of the fainter component of the 6.0 d eclipsing binary in the optical region of the spectrum, measured their radial velocities, and derived a mass ratio of M Ac2 / M Ac1  = 1.29, which is the opposite of what was estimated from the International Ultraviolet explorer (IUE) spectra. We confirm that the eclipsing subsystem Ac is semi-detached and is therefore in a phase of large-scale mass transfer between its components. The Roche-lobe filling and spectroscopically brighter component Ac1 is the less massive of the two and is eclipsed in the secondary minimum. We show that the bulk of the H α emission, so far believed to be associated with the eclipsing system, moves with the primary O9.7 I component Aa1 of the 20.73 d spectroscopic binary. However, the weak emission in the higher Balmer lines seems to be associated with the accretion disc around component Ac2. We demonstrate that accurate masses and other basic physical properties including the distance of this unique system can be obtained but require a more sophisticated modelling. A first step in this direction is presented in the accompanying Paper II (Brož et al.). 

Abstract IceCube is a Cherenkov detector instrumenting over a cubic kilometer of glacial ice deep under the surface of the South Pole. The DeepCore sub-detector lowers the detection energy threshold to a few GeV, enabling the precise measurements of neutrino oscillation parameters with atmospheric neutrinos. The reconstruction of neutrino interactions inside the detector is essential in studying neutrino oscillations. It is particularly challenging to reconstruct sub-100 GeV events with the IceCube detectors due to the relatively sparse detection units and detection medium. Convolutional neural networks (CNNs) are broadly used in physics experiments for both classification and regression purposes. This paper discusses the CNNs developed and employed for the latest IceCube-DeepCore oscillation measurements [1]. These CNNs estimate various properties of the detected neutrinos, such as their energy, direction of arrival, interaction vertex position, flavor-related signature, and are also used for background classification. 

The IceCube Upgrade is an extension of the existing IceCube Neutrino Observatory and will be deployed in the 2025–2026 austral summer. It will significantly improve the sensitivity of the detector to atmospheric neutrino oscillations. The existing 86-string IceCube array contains a dense in-fill known as DeepCore which is optimized to measure neutrinos with energies down to a few GeV. The IceCube Upgrade will consist of seven new densely instrumented strings placed within the DeepCore volume to further enhance the performance in the GeV energy range. The additional strings will feature new optical modules, each containing multiple photomultiplier tubes (PMTs), in contrast to the existing modules that each contain a single PMT. This will more than triple the number of PMT channels with respect to the current IceCube configuration, allowing for improved detection efficiency and reconstruction performance at GeV energies. We describe necessary updates to simulation, event selection, and reconstruction to accommodate the higher data rates observed by the upgraded detector and the addition of multi-PMT modules. We determine the expected sensitivity of the IceCube Upgrade to the atmospheric neutrino oscillation parameters ${\sin }^2{\theta }_{23}$and $\mathrm{\Delta }{m}_{32}^2$, the appearance of tau neutrinos and the neutrino mass ordering. The IceCube Upgrade will provide neutrino oscillation measurements that are of similar precision to those from accelerator experiments, while providing complementarity by probing higher energies and longer baselines, and with different sources of systematic uncertainties. 

Abstract Recently, IceCube reported neutrino emission from the Seyfert galaxy NGC 1068. Using 13.1 yr of IceCube data, we present a follow-up search for neutrino sources in the northern sky. NGC 1068 remains the most significant neutrino source among 110 preselected gamma-ray emitters while also being spatially compatible with the most significant location in the northern sky. Its energy spectrum is characterized by an unbroken power-law with spectral indexγ = 3.4 ± 0.2. Consistent with previous results, the observed neutrino flux exceeds its gamma-ray counterpart by at least 2 orders of magnitude. Motivated by this disparity and the high X-ray luminosity of the source, we selected 47 X-ray-bright Seyfert galaxies from the Swift/BAT spectroscopic survey that were not included in the list of gamma-ray emitters. When testing this collection for neutrino emission, we observe a 3.3σexcess from an ensemble of 11 sources, with NGC 1068 excluded from the sample. Our results strengthen the evidence that X-ray-bright cores of active galactic nuclei are neutrino emitters. 

Abstract The IceCube Neutrino Observatory has observed extragalactic astrophysical neutrinos with an apparently isotropic distribution. Only a small fraction of the observed astrophysical neutrinos can be explained by known sources. Neutrino production is thought to occur in energetic environments that are ultimately powered by the gravitational collapse of dense regions of the large-scale mass distribution in the universe. Whatever their identity, neutrino sources likely trace this large-scale mass distribution. The clustering of neutrinos with a tracer of the large-scale structure may provide insight into the distribution of neutrino sources with respect to redshift and the identity of neutrino sources. We implement a two-point angular cross correlation of the Northern sky track events with an infrared galaxy catalog derived from the Wide-field Infrared Survey Explorer (WISE) and Two Micron All Sky Survey (2MASS) source catalogs, which trace the nearby large-scale structure. No statistically significant correlation is found between the neutrinos and this infrared galaxy catalog. We find that ≤54% of the diffuse muon neutrino flux can be attributed to sources correlated with the galaxy catalog with 90% confidence. Additionally, when assuming that the neutrino source comoving density evolves following a power law in redshift,dN_s/dV ∝ (1 + z)^k, we find that sources with negative evolution, in particulark < −1.75, are disfavored at the 90% confidence level.