VAE-based latent-space classification of RNO-G data

RNO-G Collaboration

Research output: Contribution to journalConference articlepeer-review

Abstract

The Radio Neutrino Observatory in Greenland (RNO-G) is a radio-based ultra-high energy neutrino detector located at Summit Station, Greenland. It is still being constructed, with 7 stations currently operational. Neutrino detection works by measuring Askaryan radiation produced by neutrino-nucleon interactions. A neutrino candidate must be found amidst other backgrounds which are recorded at much higher rates—including cosmic-rays and anthropogenic noise—the origins of which are sometimes unknown. Here we describe a method to classify different noise classes using the latent space of a variational autoencoder. The latent space forms a compact representation that makes classification tractable. We analyze data from a noisy and a silent station. The method automatically detects and allows us to qualitatively separate multiple event classes, including physical wind-induced signals, for both the noisy and the quiet station.

Original languageEnglish (US)
Article number1056
JournalProceedings of Science
Volume444
StatePublished - Sep 27 2024
Event38th International Cosmic Ray Conference, ICRC 2023 - Nagoya, Japan
Duration: Jul 26 2023Aug 3 2023

All Science Journal Classification (ASJC) codes

  • General

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