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This Letter extends a recent IceCube sterile neutrino search to include unstable sterile neutrinos within the context of a model termed $3+1+\mathrm{Decay}$ , which expands upon the $3+1$ model by introducing sterile neutrino decay to invisible particles with coupling constant $g^2$ . The model is attractive since it reduces tension between oscillation experiments within the global fits and with constraints that come from cosmological observables. The analysis uses 10.7 years of up-going muon neutrino data with energy 500 GeV to 100 TeV and with improved reconstruction and modeling of systematics. The best-fit point is found to be $g^2=0$ , ${\sin }^2\left(2{\theta }_{24}\right)=0.16$ , and $\mathrm{\Delta }{m}_{41}^2=3.5{\mathrm{eV}}^2$ , in agreement with the recent $3+1$ sterile neutrino search. Values of $g^2\ge \pi$ are excluded at 95% confidence level. This result substantially limits decay parameter space indicated by recent global fits, disfavoring the decay scenario. 

Understanding the nature and identity of dark matter is a key goal in the physics community. In the case that TeV-scale dark matter particles decay or annihilate into standard model particles, very-high-energy (VHE) gamma rays (greater than 100 GeV) will be present in the final state. The Very Energetic Radiation Imaging Telescope Array System (VERITAS) is an imaging atmospheric Cherenkov telescope array that can indirectly detect VHE gamma rays in an energy range of 100 GeV to > 30 TeV. Dwarf spheroidal galaxies (dSphs) are ideal candidates in the search for dark matter due to their high dark matter content, high mass-to-light ratios, and their low gamma-ray fluxes from astrophysical processes. This study uses a legacy data set of 638 hours collected on 17 dSphs, built over 11 years with an observing strategy optimized according to the dark matter content of the targets. The study addresses a broad dark matter particle mass range, extending from 200 GeV to 30 PeV. In the absence of a detection, we set the upper limits on the dark matter velocity-weighted annihilation cross section 

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. 

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 G106.3+2.7, commonly considered to be a composite supernova remnant (SNR), is characterized by a boomerang-shaped pulsar wind nebula (PWN) and two distinct (“head” and “tail”) regions in the radio band. A discovery of very-high-energy gamma-ray emission (E_γ> 100 GeV) followed by the recent detection of ultrahigh-energy gamma-ray emission (E_γ> 100 TeV) from the tail region suggests that G106.3+2.7 is a PeVatron candidate. We present a comprehensive multiwavelength study of the Boomerang PWN (100″ around PSR J2229+6114) using archival radio and Chandra data obtained two decades ago, a new NuSTAR X-ray observation from 2020, and upper limits on gamma-ray fluxes obtained by Fermi-LAT and VERITAS observatories. The NuSTAR observation allowed us to detect a 51.67 ms spin period from the pulsar PSR J2229+6114 and the PWN emission characterized by a power-law model with Γ = 1.52 ± 0.06 up to 20 keV. Contrary to the previous radio study by Kothes et al., we prefer a much lower PWNB-field (B∼ 3μG) and larger distance (d∼ 8 kpc) based on (1) the nonvarying X-ray flux over the last two decades, (2) the energy-dependent X-ray size of the PWN resulting from synchrotron burn-off, and (3) the multiwavelength spectral energy distribution (SED) data. Our SED model suggests that the PWN is currently re-expanding after being compressed by the SNR reverse shock ∼1000 yr ago. In this case, the head region should be formed by GeV–TeV electrons injected earlier by the pulsar propagating into the low-density environment. 

Abstract IceCube recently reported the observation of TeV neutrinos from the nearby Seyfert galaxy NGC 1068, and the corresponding neutrino flux is significantly higher than the upper limit implied by observations of GeV–TeV gamma rays. This suggests that neutrinos are produced near the supermassive black hole, where the radiation density is high enough to obscure gamma rays. We use a set of muon neutrinos with interaction vertices inside the detector, which have good sensitivity to sources in the southern sky, from IceCube data recorded between 2011 and 2021. We then search for individual and collective neutrino signals from 14 Seyfert galaxies in the southern sky selected from the Swift Burst Alert Telescope AGN Spectroscopic Survey. Using the correlations between keV X-rays and TeV neutrinos predicted by disk–corona models, and assuming production characteristics similar to NGC 1068, a collective neutrino signal search reveals an excess of $6.7_{-3.2}^{+4.0}$ events, which is inconsistent with background expectations at the 3σlevel of significance. In this Letter, we present new independent evidence that Seyfert galaxies contribute to the extragalactic flux of high-energy neutrinos.