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This is an index of the contributions by the Giant Radio Array for Neutrino Detection (GRAND) Collaboration to the 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (ARENA 2024, University of Chicago, June 11-14, 2024). The contributions include an overview of GRAND in its present and future incarnations, methods of radio-detection that are being developed for them, and ongoing joint work between the GRAND and BEACON experiments., Comment: Note: To access the list of contributions, please follow the "HTML" link that can be found on the arXiv page

Neutrinos at ultrahigh energies can originate both from interactions of cosmic rays at their acceleration sites and through cosmic-ray interactions as they propagate through the universe. These neutrinos are expected to have a low flux which drives the need for instruments with large effective areas. Radio observations of the inclined air showers induced by tau neutrino interactions in rock can achieve this, because radio waves can propagate essentially unattenuated through the hundreds of kilometers of atmosphere. Proposed arrays for radio detection of tau neutrinos focus on either arrays of inexpensive receivers distributed over a large area, the GRAND concept, or compact phased arrays on elevated mountains, the BEACON concept, to build up a large detector area with a low trigger threshold. We present a concept that combines the advantages of these two approaches with a trigger driven by phased arrays at a moderate altitude (1 km) and sparse, high-gain outrigger receivers for reconstruction and background rejection. We show that this design has enhanced sensitivity at 100 PeV over the two prior designs with fewer required antennas and discuss the need for optimized antenna designs., Comment: ARENA2024 Conference Proceeding PoS(ARENA2024)058

This special issue of the Astroparticle Physics journal is dedicated to the memory of Thomas Korff Gaisser, the Martin A. Pomerantz Professor Emeritus of Physics at the University of Delaware. Tom was one of the most prominent scientists in cosmic-ray and astroparticle physics, and also one of the founding editors of this very journal. A theoretical particle physicist by training, he dedicated his career to cosmic-ray physics. He worked on the phenomenology of high-energy particle interactions in the atmosphere, modeling extensive air showers, including the famous 'Gaisser-Hillas' function describing their longitudinal profile. A focus of his work were muon and neutrino fluxes resulting from cosmic-ray showers in the atmosphere, and their measurement with specialized experiments at the South Pole., Comment: Editorial of Special Issue in Astroparticle Physics journal in memory of Tom Gaisser. Link to Special Issue: https://www.sciencedirect.com/special-issue/10Q7TR3833C

In 2012 Paule and Radu proved a difficult family of congruences modulo powers of 5 for Andrews' 2-colored generalized Frobenius partition function. The family is associated with the classical modular curve of level 20. We demonstrate the existence of a congruence family for a related generalized Frobenius partition function associated with the same curve. We construct an isomorphism between this new family and the original family of congruences via a mapping on the associated rings of modular functions. The pairing of the congruence families provides a new strategy for future work on congruences associated with modular curves of composite level. We show how a similar approach can be made to multiple other recent examples in the literature. We also give some important insights into the behavior of these congruence families with respect to the Atkin--Lehner involution which proved very important in Paule and Radu's original proof., Comment: Mathematica supplement may be found online at https://www.d.umn.edu/~jsellers/garvansellerssmoot2024.pdf or through email

The surface array of IceCube, IceTop, operates primarily as a cosmic-ray detector, as well as a veto for astrophysical neutrino searches for the IceCube in-ice instrumentation. However, the snow accumulation on top of the IceTop detectors increases the detection threshold and attenuates the measured IceTop signals. Enhancing IceTop by a hybrid array of scintillation detectors and radio antennas will lower the energy threshold for air-shower measurements, provide more efficient veto capabilities, enable more accurate cosmic-ray measurements, and improve the detector calibration by compensating for snow accumulation. After the initial commissioning period, a prototype station at the South Pole has been recording air-shower data and has successfully observed coincident events of both the scintillation detectors and the radio antennas with the IceTop array. The production and calibration of the detectors for the full planned array has been ongoing. Additionally, one station each has been installed at the Pierre Auger Observatory and the Telescope Array for further R\&D of these detectors in different environmental conditions. This contribution will present the status and future plans of the hybrid detector stations for the IceCube Surface Array Enhancement., Comment: Presented at the 38th International Cosmic Ray Conference (ICRC2023). See arXiv:2307.13047 for all IceCube contributions

Filter-decomposition-based group equivariant convolutional neural networks show promising stability and data efficiency for 3D image feature extraction. However, the existing filter-decomposition-based 3D group equivariant neural networks rely on parameter-sharing designs and are mostly limited to rotation transformation groups, where the chosen spherical harmonic filter bases consider only angular orthogonality. These limitations hamper its application to deep neural network architectures for medical image segmentation. To address these issues, this paper describes a non-parameter-sharing affine group equivariant neural network for 3D medical image segmentation based on an adaptive aggregation of Monte Carlo augmented spherical Fourier Bessel filter bases. The efficiency and flexibility of the adopted non-parameter-sharing strategy enable for the first time an efficient implementation of 3D affine group equivariant convolutional neural networks for volumetric data. The introduced spherical Bessel Fourier filter basis combines both angular and radial orthogonality for better feature extraction. The 3D image segmentation experiments on two abdominal medical image sets, BTCV and the NIH Pancreas datasets, show that the proposed methods excel the state-of-the-art 3D neural networks with high training stability and data efficiency. The code will be available at https://github.com/ZhaoWenzhao/WMCSFB.

An extensive library of symptom inventories has been developed over time to measure clinical symptoms, but this variety has led to several long standing issues. Most notably, results drawn from different settings and studies are not comparable, which limits reproducibility. Here, we present an artificial intelligence (AI) approach using semantic textual similarity (STS) to link symptoms and scores across previously incongruous symptom inventories. We tested the ability of four pre-trained STS models to screen thousands of symptom description pairs for related content - a challenging task typically requiring expert panels. Models were tasked to predict symptom severity across four different inventories for 6,607 participants drawn from 16 international data sources. The STS approach achieved 74.8% accuracy across five tasks, outperforming other models tested. This work suggests that incorporating contextual, semantic information can assist expert decision-making processes, yielding gains for both general and disease-specific clinical assessment.

Using CORSIKA simulations, we investigate the mass sensitivity of cosmic-ray air-shower observables for sites at the South Pole and Malarg\"ue, Argentina, the respective locations of the IceCube Neutrino Observatory and the Pierre Auger Observatory. Exact knowledge of observables from air-shower simulations was used to study the event-by-event mass separation between proton, helium, oxygen, and iron primary cosmic rays with a Fisher linear discriminant analysis. Dependencies on the observation site as well as the energy and zenith angle of the primary particle were studied in the ranges from $10^{16.0}-10^{18.5}\,$eV and $0^\circ$ to $60^\circ$: they are mostly weak and do not change the qualitative results. Promising proton-iron mass separation is achieved using combined knowledge of all studied observables, also when typical reconstruction uncertainties are accounted for. However, even with exact measurements, event-by-event separation of intermediate-mass nuclei is challenging and better methods than the Fisher discriminant and/or the inclusion of additional observables will be needed. As an individual observable, high-energy muons ($> 500\,$GeV) provide the best event-by-event mass discrimination, but the combination of muons of any energy and $X_{\text{max}}$ provides already a high event-by-event separation between proton-iron primaries at both sites. We also confirm that the asymmetry and width parameters of the air-shower longitudinal profile, $R$ and $L$, are mass sensitive. Only $R$ seems to be suitable for event-by-event mass separation, but $L$ can potentially be used to statistically determine the proton-helium ratio. Overall, our results motivate the coincident measurement of several air-shower observables, including at least $X_{\text{max}}$ and the sizes of the muonic and electromagnetic shower components, for the next generation of air-shower experiments.

Signal-to-noise ratios are a widely used concept for astroparticle radio detectors, such as air-shower radio arrays for cosmic-ray measurements or detectors searching for radio signals induced by neutrino interactions in ice. Nonetheless, no common standards or methods are established for the determination of the signal-to-noise ratio: values cannot be compared between experiments, and for the same signal and noise, various methods differ by large factors on the signal-to-noise ratio. This was the motivation to discuss a community-specific standardization at the ARENA conference 2022. No agreement on a common method to calculate signal-to-noise ratios was reached, however, awareness was raised that signal-to-noise ratios need to be well defined in any publications. This includes providing sufficient information on the procedure used to determine the signal-to-noise ratio, in addition to simply stating the formula. Even when using the same definition of the signal-to-noise ratio, there is still a significant dependence on the procedure of calculation, e.g., the signal-to-noise ratio of waveforms containing only background can vary significantly depending on the size of the time interval used as signal search window. To facilitate the interpretation of any signal-to-noise ratios in a specific study, the recommendation is to also state the mean value of the signal-to-noise ratio that the used method yields when applied to noise used in the study, e.g., the radio background measured by the corresponding experiment., Comment: Proceedings of ARENA 2022

IceCube-Gen2, the next generation of the IceCube Neutrino Observatory at the South Pole, will consist of three co-located arrays: a deep Optical Array and a more shallow and larger Radio Array for neutrino detection in the ice, and a Surface Array above the footprint of the Optical Array. The Surface Array will be comprised of hybrid stations featuring elevated radio antennas and scintillation detectors, following the design of a prototype station successfully operating at the South Pole since 2020. Besides providing a veto for neutrino detection, the Surface Array will make IceCube-Gen2 a unique laboratory for cosmic-ray air showers. Compared to the current IceCube detector with its IceTop surface array, the aperture for coincident air-shower measurements detected by both, the deep optical and surface arrays, will increase by about a factor of 30. In addition to particle physics questions, such as the production of PeV muons and neutrinos in prompt decays, these surface-deep coincidences will be used to target astrophysical questions of the most energetic Galactic cosmic rays. The combination of particle and radio measurements at the surface and high-energy muons measured in the ice promises unprecedented accuracy for the mass composition in the energy range of the presumed Galactic-to-extragalactic transition - complementing the multimessenger science case of IceCube-Gen2. This proceeding provides an overview of the IceCube-Gen2 Surface Array and, in particular, its radio component., Comment: Proceeding of ARENA 2022

Group-equivariant convolutional neural networks (G-CNN) heavily rely on parameter sharing to increase CNN's data efficiency and performance. However, the parameter-sharing strategy greatly increases the computational burden for each added parameter, which hampers its application to deep neural network models. In this paper, we address these problems by proposing a non-parameter-sharing approach for group equivariant neural networks. The proposed methods adaptively aggregate a diverse range of filters by a weighted sum of stochastically augmented decomposed filters. We give theoretical proof about how the continuous group convolution can be approximated by our methods. Our method applies to both continuous and discrete groups, where the augmentation is implemented using Monte Carlo sampling and bootstrap resampling, respectively. We demonstrate that our methods serve as an efficient extension of standard CNN. Experiments on group equivariance tests show how our methods can achieve superior performance to parameter-sharing group equivariant networks. Experiments on image classification and image denoising tasks show that in certain scenarios, with a suitable set of filter bases, our method helps improve the performance of standard CNNs and build efficient lightweight image denoising networks. The code will be available at https://github.com/ZhaoWenzhao/MCG_CNN.