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The planned IceCube-Gen2 radio neutrino detector at the South Pole will enhance the detection of cosmic ultra-high-energy neutrinos. It is crucial to utilize the available time until construction to optimize the detector design. A fully differentiable pipeline, from signal generation to detector response, would allow for the application of gradient descent techniques to explore the parameter space of the detector. In our work, we focus on the aspect of signal generation, and propose a modularized deep learning architecture to generate radio signals from in-ice neutrino interactions conditioned on the shower energy and viewing angle. The model is capable of generating differentiable signals with amplitudes spanning multiple orders of magnitude, as well as consistently producing signals corresponding to the same underlying event for different viewing angles. The modularized approach ensures physical consistency of the samples and leads to advantageous computational properties when using the model as part of a bigger optimization pipeline.

The powerful jets of blazars have been historically considered as likely sites of high-energy cosmic-ray acceleration. However, the particulars of the launched jet and the locations of leptonic and hadronic jet loading remain unclear. In the case when leptonic and hadronic particle injection occur jointly, a temporal correlation between synchrotron radiation and neutrino production is expected. We use a first catalog of millimeter wavelength (95-225 GHz) blazar light curves from the Atacama Cosmology Telescope for a time-dependent correlation with 12 yr of muon neutrino events from the IceCube South Pole Neutrino Observatory. Such millimeter emission traces activity of the bright jet base, which is often self-absorbed at lower frequencies and potentially gamma-ray opaque. We perform an analysis of the population, as well as analyses of individual, selected sources. We do not observe a significant signal from the stacked population. TXS 0506+056 is found as the most significant, individual source, though this detection is not globally significant in our analysis of selected active galactic nuclei. Our results suggest that the majority of millimeter-bright blazars are neutrino dim. In general, it is possible that many blazars have lighter, leptonic jets, or that only selected blazars provide exceptional conditions for neutrino production.

Context. We developed a probabilistic machine learning method with the aim of performing the O(10)-way classification of low- and high-resolution spectra of stellar and extragalactic targets for the upcoming 4MOST survey. In fulfilment of the survey requirements, this method should be able to express uncertainty in the input data as well as uncertainty introduced in its prediction. Methods. Four different methods are explored: (1) convolutional neural networks (CNNs), (2) the Dirichlet distribution, (3) Monte Carlo dropout (MCD), (4) Bayesian neural Networks (BNNs) + variational inference (VI). Training and validation was performed using labelled spectra from the SDSS database and a custom 4MOST mock dataset. All the methods were compared in terms of the same metrics: accuracy, area under the curve (AUC), expected calibration error (ECE), Shannon entropy, negative log-likelihood (NLL), Brier score, training time, and inference time. Aims. A CNN with simple architecture and similar to 2 & times; 10(5) parameters was trained to achieve classification accuracies of 91.5% on SDSS data and 92.8% on 4MOST mock data. The direct Dirichlet prediction and VI models tested provide uncertainties on class membership probabilities, but they confuse classes more often. The MCD on a CNN is found to be the most suitable; it boosts the point-estimate accuracies to 92.6% and 93.9%, while still providing fast training and sufficiently fast inference. Compared to a standard CNN, the method additionally provides well-calibrated uncertainties at marginal extra cost.

The IceCube Neutrino Observatory has observed a diffuse flux of high-energy astrophysical neutrinos formore than a decade. A relevant background to the astrophysical flux is prompt atmospheric neutrinos, originating from the decay of charmed mesons produced in cosmic-ray-induced air showers. The production rate of charmed mesons in the very forward phase space of hadronic interactions, and consequently, the prompt neutrino flux, remains uncertain and has not yet been observed by neutrino detectors. An accurate measurement of this fluxwould enhance our understanding of fundamental particle physics such as hadronic interactions in high-energy cosmic-ray-induced air showers and the nucleon structure. Furthermore, an experimental characterization of this background flux will improve the precision of astrophysical neutrino flux spectral measurements. In this work, we perform a combined fit of cascade-like and track-like neutrino events in IceCube to constrain the prompt atmospheric neutrino flux. Given that the prompt flux is a sub-dominant contribution, treating systematic uncertainties arising from the potential mis-modeling of the conventional and astrophysical neutrino fluxes is critical for its measurement. Our analysis yields a non-zero best-fit result, which is, however, consistent with the null hypothesis of no prompt flux within one standard deviation. Consequently, we establish an upper bound on the flux at 4 x 10(-16) (GeV m(2) s sr)(-1) at 10TeV.

The simulation of extensive air showers and particle cascades in general is a cornerstone of modern astroparticle physics. For more than two decades, CORSIKA, currently in version 7, has been one of the most widely used tools for this purpose. However, its architecture reflects design constraints of an earlier computing era, as well as increasingly limiting extensibility, maintainability, and adaptability to modern experimental requirements. CORSIKA 8 is a complete redesign of the original CORSIKA code, implemented in modern C++ and based on contemporary software engineering principles. It introduces a modular and extensible simulation framework with explicit handling of units, flexible geometry, and environment descriptions. In this paper, we present the design philosophy and core architecture of CORSIKA 8, describe the implementation of electromagnetic and hadronic shower physics, and validate air shower simulations against CORSIKA 7. The results demonstrate good agreement at the similar to 5-10% level for key observables, confirming the physics fidelity of CORSIKA 8. The observed differences reflect the current level of intrinsic systematic uncertainties in state-of-the-art air-shower simulations. We also showcase new use cases that were beyond the capabilities of version 7, such as the simulation of cross-media showers and particle cascades in ice, including radio-signal propagation.

The detection of ultra-high-energy (UHE) neutrinos in the EeV range is the goal of current and future in-ice radio arrays at the South Pole and in Greenland. Here, we present a deep neural network that can reconstruct the main neutrino properties of interest from the raw waveforms recorded by the radio antennas: the neutrino direction, the energy of the particle shower induced by the neutrino interaction, and the event topology, thereby estimating the neutrino flavor. For the first time, we predict the full posterior PDF for the energy and direction reconstruction via neural posterior estimation utilizing conditional normalizing flows, enabling event-by-event uncertainty prediction. We improve over previous reconstruction algorithms and obtain a median resolution of 0.30 log(E) and 18 square degrees for a ‘shallow’ detector component and 0.08 log(E) and 28 square degrees for a ‘deep’ detector component for neutral current (NC) events at a shower energy of 1 EeV. This deep learning approach also allows us to reconstruct the more stochastic  - charged current (CC) events. We quantify the impact of different antenna types and systematic uncertainties on the reconstruction and derive a goodness-of-fit score to test the compatibility of measured neutrino signals with the Monte Carlo simulations used to train the neural network. 

We report improved measurements of the all flavor astrophysical neutrino spectrum with IceCube by combining complementary neutrino samples in two independent analyses. Both analyses show evidence of a harder spectrum at energies below ∼30  TeVcompared to higher energies where the spectrum is well characterized by a power law. The spectrum is better described by a log parabola or a broken power law, the latter being the preferred model. Both, however, reject a single power law over an energy range 5 TeV–10 PeV with a significance >4⁢𝜎, providing new constraints on properties of cosmic neutrino sources.

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 3 sigma level of significance. In this Letter, we present new independent evidence that Seyfert galaxies contribute to the extragalactic flux of high-energy neutrinos.

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.

We present GollumFit, a framework designed for performing binned-likelihood analyses on neutrino telescope data. GollumFit incorporates model parameters common to any neutrino telescope and also model parameters specific to the IceCube Neutrino Observatory. We provide a high-level overview of its key features and how the code is organized. We then discuss the performance of the fitting in a typical analysis scenario, highlighting the ability to fit over tens of nuisance parameters. We present some examples showing how to use the package for likelihood minimization tasks. This framework uniquely incorporates the particular model parameters necessary for neutrino telescopes, and solves an associated likelihood problem in a time-efficient manner.