Novelty Detection on Radio Astronomy Data using Signatures

Understand

We introduce SigNova, a new semi-supervised framework for detecting anomalies in streamed data.

• While our initial examples focus on detecting radio-frequency interference (RFI) in digitized signals within the field of radio astronomy, it is important to note that SigNova's applicability extends to any type of streamed data.
• The framework comprises three primary components.
• Firstly, we use the signature transform to extract a canonical collection of summary statistics from observational sequences.