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

Autor:	Glüsenkamp, Thorsten
Rok vydání:	2023
Předmět:	Astrophysics - High Energy Astrophysical Phenomena Astrophysics - Instrumentation and Methods for Astrophysics Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Presented at the 38th International Cosmic Ray Conference (ICRC2023)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2309.16401 Zobrazit plný text záznamu View this record from Arxiv