VAE-based latent-space classification of RNO-G data
This work addresses noise classification for the RNO-G neutrino detector, which is incremental as it applies an existing VAE method to new data from this specific domain.
The paper tackled the problem of classifying noise classes in RNO-G neutrino detector data by using a variational autoencoder's latent space, achieving automatic detection and qualitative separation of multiple event classes, including physical wind-induced signals, for both noisy and quiet stations.
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.