The Radio Neutrino Observatory in Greenland (RNO-G) is the first in-ice radio array in the northern hemisphere for the detection of ultra-high energy neutrinos via the coherent radio emission from neutrino-induced particle cascades within the ice. The array is currently in phased construction near Summit Station on the Greenland ice sheet, with 7 stations deployed during the first two boreal summer field seasons of 2021 and 2022. In this paper, we describe the installation and system design of these initial RNO-G stations, and discuss the performance of the array as of summer 2024.

The Radio Neutrino Observatory – Greenland (RNO-G) seeks discovery of ultra-high energy neutrinos from the cosmos through their interactions in ice. The science program extends beyond particle astrophysics to include radioglaciology and, as we show herein, solar observations, as well. Currently seven of 35 planned radio-receiver stations (24 antennas/station) are operational. These stations are sensitive to impulsive radio signals with frequencies between 80 and 700 MHz and feature a neutrino trigger threshold for recording data close to the thermal floor. RNO-G can also trigger on elevated signals from the Sun, resulting in nanosecond resolution time-domain flare data; such temporal resolution is significantly shorter than from most dedicated solar observatories. In addition to possible RNO-G solar flare polarization measurements, the Sun also represents an extremely useful above-surface calibration source.Using RNO-G data recorded during the summers of 2022 and 2023, we find signal excesses during solar flares reported by the solar-observing Callisto network and also in coincidence with ∼2/3 of the brightest excesses recorded by the SWAVES satellite. These observed flares are characterized by significant time-domain impulsivity. Using the known position of the Sun, the flare sample is used to calibrate the RNO-G absolute pointing on the radio signal arrival direction to sub-degree resolution. We thus establish the Sun as a regularly observed astronomical calibration source to provide the accurate absolute pointing required for neutrino astronomy.

IceCube-Gen2 is a planned next-generation neutrino observatory at the South Pole that builds upon the successful design of IceCube. Integrating two complementary detection technologies for neutrinos, optical and radio Cherenkov emission, in combination with a surface array for cosmic ray air shower detection, IceCube-Gen2 will cover a broad neutrino energy range from MeV to EeV. This index of contributions to the 38th International Cosmic Ray Conference in Nagoya, Japan (July 26 - August 3, 2023) describes research and development efforts for IceCube-Gen2. Included are summaries of the design, status, and sensitivity of the IceCube-Gen2 optical, surface, and radio components; performance studies of next-generation optical sensors detecting optical Cherenkov radiation from cosmic ray and neutrino events; reconstruction techniques of radio and optical events in terms of energy, direction, and neutrino flavor; and sensitivity studies of astrophysical neutrino flavors, diffuse neutrino fluxes, and cosmic ray anisotropies. Contributions related to IceCube and the scheduled IceCube Upgrade are available in a separate collection.

Muonic atom X-ray Emission Spectroscopy (mu XES) is a novel technique in the broad field of non-destructive methods for cultural heritage analysis. It relies on the interaction of a probe of negative muons with matter and following emission of X-ray radiation. Since the muon mass is about 207 times bigger than the electron, these emitted X-rays are highly energetic and are characteristic of the emitting atom, making it possible to cover a wide part of the periodic table (from lithium to uranium). The multi-elemental range, a negligible self-absorption of the X-rays and very low residual activity left in the sample after irradiation make mu XES a very powerful probe for material characterization. In addition, by coupling the data analysis with Monte Carlo simulation methods, it is possible to asses the depth of the layers that are present in a given sample. In this work, preliminary results of the analysis on two gilded surfaces are reported.

The Radio Neutrino Observatory in Greenland (RNO-G) seeks to detect the Askaryan radio emission from energetic neutrinos (> 10 PeV) interacting in the Greenland ice sheet. The RNO-G detector comprises an array of independent and autonomous radio-detector stations, each with a hybrid design composed of deep borehole ( ∼ 100m) and surface antennas, which require low-power, robust, and scalable low-noise radiofrequency (RF) amplifier and signal-transport systems over a ∼ 80-650MHz bandwidth. In this contribution, we will present the design and performance of the custom RNO-G RF signal chains, including a field-proven and low-cost RF-over-fiber (RFoF) unit

Radio detection of ultra-high energy neutrinos via the Askaryan effect has enabled a new generation of immense, cost-effective neutrino detectors due to the long attenuation lengths of radio signals in dense dielectric media. The Radio Neutrino Observatory in Greenland (RNO-G) is one such detector with a unique view of the northern hemisphere; it is being built at the top of Greenland’s ice cap to take advantage of the low noise environment and large detection volume available in the pure ice. It currently has 7 fully operational independent autonomous stations spaced roughly 1 km apart and once completed will comprise 35 stations. Each station contains both a shallow component with broadband, high-gain antennas and an in-ice component with three strings of broadband, omnidirectional horizontally and vertically polarized antennas. Both polarization modes are needed to reconstruct the arrival direction of detected neutrinos. Here we discuss the design, simulation, validation, production, deployment, and performance of RNO-G’s horizontally polarized antennas.

In the hopes of observing the highest-energy neutrinos (E> 1 EeV) populating the Universe, both past (RICE, AURA, ANITA) and current (RNO-G, ARIANNA, ARA and TAROGE-M) polar-sited experiments exploit the impulsive radio emission produced by neutrino interactions. In such experiments, rare single event candidates must be unambiguously identified above backgrounds. Background rejection strategies to date primarily target thermal noise fluctuations and also impulsive radio-frequency signals of anthropogenic origin. In this paper, we consider the possibility that 'fake' neutrino signals may also be generated naturally via the `triboelectric effect' This broadly describes any process in which force applied at a boundary layer results in displacement of surface charge, leading to the production of an electrostatic potential difference AV. Wind blowing over granular surfaces such as snow can induce such a potential difference, with subsequent coronal discharge. Discharges over timescales as short as nanoseconds can then lead to radio-frequency emissions at characteristic MHz-GHz frequencies. Using data from various past (RICE, AURA, SATRA, ANITA) and current (RNO G, ARIANNA and ARA) neutrino experiments, we find evidence for such backgrounds, which are generally characterized by: (a) a threshold wind velocity which likely depends on the experimental trigger criteria and layout; for the experiments considered herein, this value is typically O(10 m/s), (b) frequency spectra generally shifted to the low-end of the frequency regime to which current radio experiments are typically sensitive (100-200 MHz), (c) for the strongest background signals, an apparent preference for discharges from above-surface structures, although the presence of more isotropic, lower amplitude triboelectric discharges cannot be excluded.

The Radio Neutrino Observatory – Greenland (RNO-G) is an in-ice neutrino detector currently under construction. The detector is designed to make the first measurement of neutrinos beyond energies of $\sim$10 PeV. Each of the planned 35 stations of the detector includes three log-periodic dipole array antennas (LPDA) pointing towards the sky. The stations cover an area of $\sim$ 50 km$^2$ and enable RNO-G to measure the radio emission of cosmic-ray induced air-showers, thus making it a cosmic-ray detector as well. As other experiments have shown, such radio emission can be used to make precision cosmic-ray measurements. Additionally, the location of the experiment at Summit Station, at a height of $\sim$3000 m, enables RNO-G to study the phenomena of shower cores hitting the air/ice boundary and further developing in the ice itself. Moreover, RNO-G is also able to study high energetic muons, created in cosmic-ray induced air-showers, which penetrate into the ice from above. In this contribution, we will give an overview of the cosmic-ray analysis of RNO-G and report the current status. This includes outlining the method used for identifying the air-shower signals using signal templates, showing the first cosmic-ray candidate events and discussing systematic uncertainties.

The Radio Neutrino Observatory in Greenland (RNO-G) is an array of radio detector stations which has been designed to study ultra-high energy (𝐸 ≳ $10^{18}$ eV) neutrinos. The experiment, when completed, will have the best sensitivity in this energy range and will yield a major advancement in our understanding of the sources and propagation of the highest energy cosmic rays. While RNO-G will be sensitive to primarily 𝐸 ≳ 100 PeV neutrinos, the optical-based detectors only have a large enough exposure to study up to ∼ 1–10 PeV, leaving a gap in the energy range between the two detection methods. For RNO-G, the energy threshold is set by our ability to distinguish the Askaryan pulses, created from neutrino interactions, from the irreducible background of thermal noise. Using modern machine learning techniques, an online trigger can be implemented to identify small-amplitude pulses from in-ice cascades and thereby decrease the energy threshold of RNO-G. Such an advancement will increase the expected amount of observed neutrinos, as well as close the gap between radio- and optical-based observatories. We present a convolutional neural network for classification of neutrino events that can be run as a second-stage trigger.

Muonic atom X-ray Emission Spectroscopy (µ-XES) is a novel technique based on the detection of high-energy X-rays emitted after the interaction of a negative muon beam with matter. Thanks to the multi-elemental range, negligible self-absorption of the x-rays and the possibility of performing depth profile studies, the technique is a very powerful probe for the analysis of cultural heritage artefacts. By tuning the energy of the incident muon beam, indeed, it is possible to investigate the different layers that constitute a sample. In this work, we report preliminary results of the analysis on two fire-gilded surfaces, in which the data analysis is coupled with Monte Carlo-based simulation software. Here, to perform a depth profile characterization, the samples were analysed at different beam energies (or momentum). Each of the resulting x-ray spectra was then analysed and compared with the output of the simulations software and a remarkably good agreement was reached. The results of the work are promising and with this approach, it will be possible to enhance the capability offered by the technique, both in terms of data analysis and data interpretation.

Glacial ice is used as a target material for the detection of ultra-high energy neutrinos, by measuring the radio signals that are emitted when those neutrinos interact in the ice. Thanks to the large attenuation length at radio frequencies, these signals can be detected over distances of several kilometers. One experiment taking advantage of this is the Radio Neutrino Observatory Greenland (RNO-G), currently under construction at Summit Station, near the apex of the Greenland ice sheet. These experiments require a thorough understanding of the dielectric properties of ice at radio frequencies. Towards this goal, calibration campaigns have been undertaken at Summit, during which we recorded radio reflections off internal layers in the ice sheet. Using data from the nearby GISP2 and GRIP ice cores, we show that these reflectors can be associated with features in the ice conductivity profiles; we use this connection to determine the index of refraction of the bulk ice as n=1.778 +/- 0.006.

After two seasons of installation, 7 stations built and many lessons learned, the Radio Neutrino Observatory Greenland (RNO-G) is now operational. In the coming years, the construction of another 28+ stations will bring the array to full capacity as an instrument with an eye towards the ultra-high energy neutrino (>10 PeV) regime, creating another link in the fast paced and rapidly changing landscape of multi-messenger astronomy. Until now, the data volume of our two initial seasons has remained manageable. However, as the array continues to grow, we need to develop faster and more efficient processes regarding how to filter our data; we must throw away the noise and identify the most promising events. Data reduction tools become crucial for anthropogenic, environmental and local noise identification/removal in order to test and monitor our instrument as we scale up. We present a convolutional encoder-decoder network that assigns an anomaly ranking to events, helping to classify different categories of background and signal.

For the in-ice component of the next generation neutrino observatory at the South Pole, IceCube-Gen2, a new sensor module is being developed, which is an evolution of the D-Egg and mDOM sensors developed for the IceCube Upgrade. The sensor design features up to 18 4-inch PMTs distributed homogeneously in a borosilicate glass pressure vessel. Challenges arise for the mechanical design from the tight constraints on the bore hole diameter (which will be 2 inches smaller than for IceCube Upgrade) and from the close packing of the PMTs. The electronics design must meet the space constraints posed by the mechanical design as well as the power consumption and cost considerations from over 10,000 optical modules being deployed. This contribution presents forward-looking solutions to these design considerations. Prototype modules will be installed and integrated in the IceCube Upgrade.

We present performance studies of a segmented optical module for the IceCube-Gen2 detector. Based on the experience gained in sensor development for the IceCube Upgrade, the new sensor will consist of up to eighteen 4 inch PMTs housed in a transparent pressure vessel, providing homogeneous angular coverage. The use of custom molded optical `gel pads' around the PMTs enhances the photon capture rate via total internal reflection at the gel-air interface. This contribution presents simulation studies of various sensor, PMT, and gel pad geometries aimed at optimizing the sensitivity of the optical module in the face of confined space and harsh environmental conditions at the South Pole.

Abstract Since summer 2021, the Radio Neutrino Observatory in Greenland (RNO-G) is searching for astrophysical neutrinos at energies $${>10}$$ > 10 PeV by detecting the radio emission from particle showers in the ice around Summit Station, Greenland. We present an extensive simulation study that shows how RNO-G will be able to measure the energy of such particle cascades, which will in turn be used to estimate the energy of the incoming neutrino that caused them. The location of the neutrino interaction is determined using the differences in arrival times between channels and the electric field of the radio signal is reconstructed using a novel approach based on Information Field Theory. Based on these properties, the shower energy can be estimated. We show that this method can achieve an uncertainty of 13% on the logarithm of the shower energy after modest quality cuts and estimate how this can constrain the energy of the neutrino. The method presented in this paper is applicable to all similar radio neutrino detectors, such as the proposed radio array of IceCube-Gen2.

In this work we perform a population study of the H.E.S.S. Galactic Plane Survey (HGPS) catalogue. Namely, we analyze the flux, latitude and longitude distributions of gamma-ray sources detected by H.E.S.S. with the goal of inferring the main properties of galactic TeV sources population. We show that the total Milky Way luminosity in the 1-100 TeV energy range is relatively well constrained by H.E.S.S. data, obtaining !MW = 1.7+0.5 −0.4 × 10 37ergs s−1. The total Galactic flux in the H.E.S.S. observational window is Φtot = 3.8+1.0 −1.0 × 10 −10cm−2 s−1. The above results allows us to estimate the flux produced by sources not resolved byH.E.S.S. These sources, which are too faint (or too extended) to be detected by H.E.S.S., contribute to the largescale diffuse signal observed at the TeV range. We show that unresolved source contribution is not negligible (about 60% of the resolved signal measured by H.E.S.S.) and potentially responsible for a large fraction of the diffuse-large scale gamma-ray signal observed by H.E.S.S. and other experiments in the TeV domain. Finally, in the hypothesis that the majority of bright sources detected by H.E.S.S. are powered by pulsar activity, like e.g. Pulsar Wind Nebulae or TeV halos, we estimate the main properties of the pulsar population: we obtain a constrain on the fading time g, the initial period %0 and the magnetic field .

The ARIANNA experiment is an Askaryan detector designed to record radio signals induced by neutrino interactions in the Antarctic ice. Because of the low neutrino flux at high energies ($E > 10^{16} $), the physics output is limited by statistics. Hence, an increase in sensitivity significantly improves the interpretation of data and offers the ability to probe new parameter spaces. The amplitudes of the trigger threshold are limited by the rate of triggering on unavoidable thermal noise fluctuations. We present a real-time thermal noise rejection algorithm that enables the trigger thresholds to be lowered, which increases the sensitivity to neutrinos by up to a factor of two (depending on energy) compared to the current ARIANNA capabilities. A deep learning discriminator, based on a Convolutional Neural Network (CNN), is implemented to identify and remove thermal events in real time. We describe a CNN trained on MC data that runs on the current ARIANNA microcomputer and retains 95 percent of the neutrino signal at a thermal noise rejection factor of $10^5$, compared to a template matching procedure which reaches only $10^2$ for the same signal efficiency. Then the results are verified in a lab measurement by feeding in generated neutrino-like signal pulses and thermal noise directly into the ARIANNA data acquisition system. Lastly, the same CNN is used to classify cosmic-rays events to make sure they are not rejected. The network classified 102 out of 104 cosmic-ray events as signal.

AbstractOver the last 25 years, radiowave detection of neutrino-generated signals, using cold polar ice as the neutrino target, has emerged as perhaps the most promising technique for detection of extragalactic ultra-high energy neutrinos (corresponding to neutrino energies in excess of 0.01 Joules, or 1017 electron volts). During the summer of 2021 and in tandem with the initial deployment of the Radio Neutrino Observatory in Greenland (RNO-G), we conducted radioglaciological measurements at Summit Station, Greenland to refine our understanding of the ice target. We report the result of one such measurement, the radio-frequency electric field attenuation length $L_\alpha$. We find an approximately linear dependence of $L_\alpha$ on frequency with the best fit of the average field attenuation for the upper 1500 m of ice: $\langle L_\alpha \rangle = ( ( 1154 \pm 121) - ( 0.81 \pm 0.14) \, ( \nu /{\rm MHz}) ) \,{\rm m}$ for frequencies ν ∈ [145 − 350] MHz.

The proposed IceCube-Gen2 (ICG2) seeks to instrument 500 sq. km of Antarctic ice near the geographic South Pole with radio antennas, in order to observe the highest energy (E>1 EeV) neutrinos in the Universe. To this end, ICG2 will use the impulsive radio-frequency (RF) signal produced by neutrino interactions in polar ice caps. In such experiments, rare single event candidates must be unambiguously separated from background; to date, signal identification strategies primarily reject thermal noise and anthropogenic backgrounds. Here, we consider the possibility that fake neutrino signals may also be naturally generated via the 'triboelectric effect'. This broadly includes any process in which force applied at a boundary layer results in displacement of surface charge, generating a potential difference ΔV. Wind blowing over granular surfaces such as snow can induce such a ΔV, with subsequent discharge. Discharges over nanosecond-timescales can then lead to RF emissions at characteristic MHz-GHz frequencies. We find that such backgrounds are evident in the several neutrino experiments considered, and are generally characterized by: a) a threshold wind velocity which likely depends on the experimental signal trigger threshold and layout; for the experiments considered herein, this value is typically O(10 m/s), b) frequency spectra generally shifted to the low-end of the frequency regime to which current radio experiments are typically sensitive (100-200 MHz), c) for the strongest background signals, an apparent preference for discharges from above-surface structures, although the presence of more isotropic, lower amplitude triboelectric discharges cannot be excluded.

H.E.S.S. has recently completed the first systematic survey of the Galactic plane in the TeV energy domain. We analyze the flux, latitude, and longitude distributions of γ-ray sources observed by H.E.S.S. in order to infer the properties of the Galactic TeV source population. We show that the total Milky Way luminosity in the 1–100 TeV energy range is . Evaluating the cumulative flux expected at Earth by the considered population, we show that H.E.S.S. unresolved sources provide a relevant contribution to the diffuse Galactic emission. Finally, in the hypothesis that the majority of bright sources detected by H.E.S.S. are powered by pulsar activity, like, e.g., pulsar wind nebulae or TeV halos, we estimate the main properties of the pulsar population.