Observation of Galactic noise and identification of background sources in RNO-G

The Radio Neutrino Observatory in Greenland (RNO-G) aims to detect EeV energy neutrinos. Operating primarily in the 200-700 MHz range, RNO-G is sensitive to broadband signals originating from many sources, including thermal noise, galactic noise, and various types of anthropogenic and environmental backgrounds. Each of these backgrounds will have a unique event signature that can be identified through reconstruction, polarization, and frequency content. During the 2021 and 2022 deployment seasons, 7 of the envisioned 35 stations have been installed and are taking data. This data lends itself well for initial analyses and the characterisation of the classes of noise recorded by RNO-G. In this contribution, I will discuss the current status of background identification with RNO-G. In particular, I will present our efforts to detect noise from our Galaxy, which might be used as a standard candle for future calibration. I will also discuss our efforts to develop new types of event classification strategies, including anomaly detection and other machine learning tools.

Authors

Publication Details

Published
2024-11-07
DOI
https://doi.org/10.22323/1.470.0005
Citations
2
Primary Topic
Geophysics and Gravity Measurements
Type
article
Field-Weighted Citation Impact
0.96
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article

Observation of Galactic noise and identification of background sources in RNO-G

Jethro Stoffels
2 citations
Geophysics and Gravity Measurements
0.96
article

Observation of Galactic noise and identification of background sources in RNO-G

Jethro Stoffels
article en
2 citations

Abstract

The Radio Neutrino Observatory in Greenland (RNO-G) aims to detect EeV energy neutrinos. Operating primarily in the 200-700 MHz range, RNO-G is sensitive to broadband signals originating from many sources, including thermal noise, galactic noise, and various types of anthropogenic and environmental backgrounds. Each of these backgrounds will have a unique event signature that can be identified through reconstruction, polarization, and frequency content. During the 2021 and 2022 deployment seasons, 7 of the envisioned 35 stations have been installed and are taking data. This data lends itself well for initial analyses and the characterisation of the classes of noise recorded by RNO-G. In this contribution, I will discuss the current status of background identification with RNO-G. In particular, I will present our efforts to detect noise from our Galaxy, which might be used as a standard candle for future calibration. I will also discuss our efforts to develop new types of event classification strategies, including anomaly detection and other machine learning tools.

Openalex Percentile: Top 24%
Geophysics and Gravity Measurements
0.96
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