Anomaly Detection in Early Data From the Radio Neutrino Observatory Greenland

Meyers ZS, Nelles A, Aguilar JA, Allison P, Besson D, Bishop A, Botner O, Bouma S, Buitink S, Castiglioni W, Cataldo M, Clark BA, Coleman A, Couberly K, Dasgupta P, de Kockere S, de Vries KD, Deaconu C, DuVernois MA, Eimer A, Glaser C, Glüsenkamp T, Hallgren A, Hallmann S, Hanson JC, Hendricks B, Henrichs J, Heyer N, Hornhuber C, Hughes K, Karg T, Karle A, Kelley JL, Korntheuer M, Kowalski M, Kravchenko I, Krebs R, Lahmann R, Lehmann P, Latif U, Laub P, Liu CH, Mammo J, Marsee MJ, Mikhailova M, Michaels K, Mulrey K, Muzio M, Novikov A, Nozdrina A, Oberla E, Oeyen B, Plaisier I, Punsuebsay N, Pyras L, Ryckbosch D, Schlüter F, Scholten O, Seckel D, Seikh MF, Smith D, Stoffels J, Southall D, Terveer K, Toscano S, Tosi D, Van Den Broeck DJ, van Eijndhoven N, Vieregg AG, Vischer J, Welling C, Williams DR, Wissel S, Young R, Zink A (2024)

Publication Type: Conference contribution

Publication year: 2024

Journal

Publisher: Sissa Medialab Srl

Book Volume: 444

Conference Proceedings Title: Proceedings of Science

Event location: Nagoya

Abstract

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.

Authors with CRIS profile

Additional Organisation(s)

Involved external institutions

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

How to cite

APA:

Meyers, Z.S., Nelles, A., Aguilar, J.A., Allison, P., Besson, D., Bishop, A.,... Zink, A. (2024). Anomaly Detection in Early Data From the Radio Neutrino Observatory Greenland. In Proceedings of Science. Nagoya, JP: Sissa Medialab Srl.

MLA:

Meyers, Z. S., et al. "Anomaly Detection in Early Data From the Radio Neutrino Observatory Greenland." Proceedings of the 38th International Cosmic Ray Conference, ICRC 2023, Nagoya Sissa Medialab Srl, 2024.

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