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

Agarwal S, Aguilar JA, Alden N, Ali S, Allison P, Betts M, Besson D, Bishop A, Botner O, Bouma S, Buitink S, Camphyn R, Chan J, Chiche S, Clark BA, Coleman A, Couberly K, de Kockere S, de Vries KD, Deaconu C, Giri P, Glaser C, Glüsenkamp T, Gui H, Hallgren A, Hallmann S, Hanson JC, Helbing K, Hendricks B, Henrichs J, Heyer N, Hornhuber C, Huesca Santiago E, Hughes K, Jaitly A, Karg T, Karle A, Kelley JL, Kopper C, Korntheuer M, Kowalski M, Kravchenko I, Krebs R, Kugelmeier M, Lahmann R, Liu CH, Marsee MJ, Mulrey K, Muzio M, Nelles A, Novikov A, Nozdrina A, Oberla E, Oeyen B, Punsuebsay N, Pyras L, Ravn M, Rifaie A, Ryckbosch D, Schlüter F, Scholten O, Seckel D, Seikh MF, Selcuk ZS, Stachurska J, Stoffels J, Toscano S, Tosi D, Tutt J, Van Den Broeck DJ, van Eijndhoven N, Vieregg AG, Vijai A, Welling C, Williams DR, Windischhofer P, Wissel S, Young R, Zink A (2025)

Publication Type: Conference contribution

Publication year: 2025

Journal

Publisher: Sissa Medialab Srl

Book Volume: 501

Conference Proceedings Title: Proceedings of Science

Event location: Geneva, CHE

DOI: 10.22323/1.501.1057

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.

Authors with CRIS profile

Involved external institutions

Ohio State University United States (USA) (US) Pennsylvania State University (Penn State) United States (USA) (US) University of Kansas (KU) United States (USA) (US) Wisconsin IceCube Particle Astrophysics Center (WIPAC) United States (USA) (US) Vrije Universiteit Brussel (VUB) Belgium (BE) University of Delaware (UDEL) United States (USA) (US) Uppsala University Sweden (SE) Universiteit Gent (UGent) / Ghent University Belgium (BE) Université libre de Bruxelles (ULB) / Free University of Brussels Belgium (BE) University of Chicago United States (USA) (US) University of Nebraska-Lincoln United States (USA) (US) Whittier College United States (USA) (US) Bergische Universität Wuppertal Germany (DE) Deutsches Elektronen-Synchrotron DESY Germany (DE) University of Maryland, College Park (UMD) United States (USA) (US) The University of Alabama United States (USA) (US) Radboud University Nijmegen Netherlands (NL)

How to cite

APA:

Agarwal, S., Aguilar, J.A., Alden, N., Ali, S., Allison, P., Betts, M.,... Zink, A. (2025). Identifying Cosmic Ray Radio Emission in RNO-G’s Deep Antennas. In Proceedings of Science. Geneva, CHE: Sissa Medialab Srl.

MLA:

Agarwal, S., et al. "Identifying Cosmic Ray Radio Emission in RNO-G’s Deep Antennas." Proceedings of the 39th International Cosmic Ray Conference, ICRC 2025, Geneva, CHE Sissa Medialab Srl, 2025.

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