Interference Meets Inference: Bayesian Time-Series Modeling of Radio-Frequency Interference

Abstract

Radio-frequency interference (RFI) remains a major challenge for modern radio astronomy experiments. In this work, we cast RFI detection as a one-dimensional time-series anomaly-detection problem and develop a probabilistic mixture-model framework for separating a smoothly varying astronomical background from anomalous contamination. The model jointly describes the clean and contaminated components and assigns each time sample a posterior probability of belonging to the RFI state, rather than relying solely on binary flags. This probabilistic formulation provides a measure of classification confidence, enables uncertainty propagation into derived downstream statistics, and offers additional information for investigating ambiguous events. We apply the framework to observations from the Murchison Widefield Array collected in 2014, producing"soft"classification labels and seasonal RFI trends. We perform a parallel analysis using the Sky-Subtracted Incoherent Noise Spectrum software pipeline (SSINS), which produces"hard"classification labels. Overall, the mixture model provides similar and in some cases superior classification results while providing a complementary probabilistic description of RFI contamination and its uncertainty.