Identifying Cosmic Ray Radio Emission in RNO-G’s Deep Antennas

Ultra-high-energy neutrinos are an invaluable messenger for learning about the most energetic and distant astrophysical processes in the universe. Detecting them is challenging due to their extremely low flux and small cross section, necessitating immense detection volumes. The Radio Neutrino Observatory in Greenland (RNO-G) addresses this challenge by leveraging the Askaryan effect, using sparse radio instrumentation to detect neutrino-induced cascades in ice. However, cosmic rays represent an important background, as their induced showers produce radio emission that can mimic neutrino signals; both in-air emission, primarily through the geomagnetic effect, and in-ice Askaryan emission can act as backgrounds for a neutrino search. At the same time, these radio emissions from cosmic-ray induced air showers provide a crucial tool for detector calibration and validation. This work presents progress toward quantifying and separating these backgrounds in RNO-G’s deep antennas using machine learning classification methods.

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Publication Details

Published
2025-09-24
DOI
https://doi.org/10.22323/1.501.1057
Primary Topic
Radio Astronomy Observations and Technology
Type
article
Field-Weighted Citation Impact
0.00

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Identifying Cosmic Ray Radio Emission in RNO-G’s Deep Antennas

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Identifying Cosmic Ray Radio Emission in RNO-G’s Deep Antennas

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article en

Abstract

Ultra-high-energy neutrinos are an invaluable messenger for learning about the most energetic and distant astrophysical processes in the universe. Detecting them is challenging due to their extremely low flux and small cross section, necessitating immense detection volumes. The Radio Neutrino Observatory in Greenland (RNO-G) addresses this challenge by leveraging the Askaryan effect, using sparse radio instrumentation to detect neutrino-induced cascades in ice. However, cosmic rays represent an important background, as their induced showers produce radio emission that can mimic neutrino signals; both in-air emission, primarily through the geomagnetic effect, and in-ice Askaryan emission can act as backgrounds for a neutrino search. At the same time, these radio emissions from cosmic-ray induced air showers provide a crucial tool for detector calibration and validation. This work presents progress toward quantifying and separating these backgrounds in RNO-G’s deep antennas using machine learning classification methods.

National Science Foundation, European Commission, Fonds De La Recherche Scientifique - FNRS, Fonds Wetenschappelijk Onderzoek, Vetenskapsrådet, British Antarctic Survey, Office of Experimental Program to Stimulate Competitive Research
Openalex Percentile: Top 82%
Radio Astronomy Observations and Technology
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