VAE-based latent-space classification of RNO-G data
Thorsten Glüsenkamp
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.