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 $$\nu _e$$ - 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.

Sweden

Nils Heyer

Germany

Christian Glaser

Thorsten Glüsenkamp

Martin Ravn

Event reconstruction for radio-based in-ice neutrino detectors with neural posterior estimation

The detection of high-energy neutrinos in the EeV range requires new detection techniques to cope with the small expected flux. The radio detection method, utilizing Askaryan emission, can be used to detect these neutrinos in polar ice. The propagation of the radio pulses has to be modeled carefully to reconstruct the energy, direction, and flavor of the neutrino from the detected radio flashes. Here, we study the effect of birefringence in ice, which splits up the radio pulse into two orthogonal polarization components with slightly different propagation speeds. This provides useful signatures to determine the neutrino energy and is potentially important to determine the neutrino direction to degree precision. We calculated the effect of birefringence from first principles where the only free parameter is the dielectric tensor as a function of position. Our code, for the first time, can propagate full RF waveforms, taking interference due to changing polarization eigenvectors during propagation into account. The model is available open-source through the NuRadioMC framework. We compare our results to in-situ calibration data from the ARA and ARIANNA experiments and find good agreement for the available time delay measurements. This indicates a significant improvement of the prediction power of birefringence effects compared to previous models. Finally, the implications and opportunities for neutrino detection are discussed.

First-principle calculation of birefringence effects for in-ice radio detection of neutrinos

Neutrino interactions with protons and neutrons probe their deep structure and may reveal new physics. The higher the neutrino energy, the sharper the probe. So far, the neutrino-nucleon (νN) cross section is known across neutrino energies from a few hundred MeV to a few PeV. Soon, ultra-high-energy (UHE) cosmic neutrinos, with energies above 100 PeV, could take us farther. So far, they have evaded discovery, but upcoming UHE neutrino telescopes endeavor to find them. We present the first detailed measurement forecasts of the UHE νN cross section, geared to IceCube-Gen2, one of the leading detectors under planning. We use state-of-the-art ingredients in every stage of our forecasts: in the UHE neutrino flux predictions, the neutrino propagation inside Earth, the emission of neutrino-induced radio signals in the detector, their propagation and detection, and the treatment of backgrounds. After 10 years, if at least a few tens of UHE neutrino-induced events are detected, IceCube-Gen2 could measure the νN cross section at center-of-mass energies of   <math> <msqrt> <mi>s</mi> </msqrt> </math>  $$ \sqrt{s} $$  ≈ 10–100 TeV for the first time, with a precision comparable to that of its theory prediction.

Denmark

Víctor Valera

Mauricio Bustamante

The ultra-high-energy neutrino-nucleon cross section: measurement forecasts for an era of cosmic EeV-neutrino discovery

We revisit a nearly 10-year old controversy on the diphoton decay of the Higgs particle. To a large extent, the controversy turned around the respective merits of the regularization techniques employed. The novel aspect of our approach is that no regularization techniques are brought to bear: we work within the Bogoliubov–Epstein–Glaser scheme of renormalization by extension of distributions. Solving the problem actually required an expansion of this method’s toolkit, furnished in the paper.

Paweł Duch

Michael Dütsch

Spain

Costa Rica

José Gracia-Bondía

Diphoton decay of the higgs from the Epstein–Glaser viewpoint

While the radio detection of cosmic rays has advanced to a standard method in astroparticle physics, the radio detection of neutrinos is just about to start its full bloom. The successes of pilot-arrays have to be accompanied by the development of modern and flexible software tools to ensure rapid progress in reconstruction algorithms and data processing. We present NuRadioReco as such a modern Python-based data analysis tool. It includes a suitable data-structure, a database-implementation of a time-dependent detector, modern browser-based data visualization tools, and fully separated analysis modules. We describe the framework and examples, as well as new reconstruction algorithms to obtain the full three-dimensional electric field from distributed antennas which is needed for high-precision energy reconstruction of particle showers.

United States

Anna Nelles

Ilse Plaisier

Christoph Welling

Steven Barwick

Daniel García-Fernández

Geoffrey Gaswint

Robert Lahmann

Christopher Persichilli

NuRadioReco: a reconstruction framework for radio neutrino detectors

Renormalization of massless Feynman amplitudes in x -space is reexamined here, using almost exclusively real-variable methods. We compute a wealth of concrete examples by means of recursive extension of distributions. This allows us to show perturbative expansions for the four-point and two-point functions at several loop order. To deal with internal vertices, we expound and expand on convolution theory for log-homogeneous distributions. The approach has much in common with differential renormalization as given by Freedman, Johnson and Latorre; but differs in important details.

José M. Gracia-Bondía

Heidy Gutiérrez

Joseph C. Várilly

Improved Epstein–Glaser renormalization in x -space versus differential renormalization