Machine learning approach to radio frequency interference(RFI) classification in radio astronomy

Radio frequency interference (RFI) presents a large problem for radio tele- scopes. Interference prevents observations from being made, or extends the duration required for observations. This thesis investigates di erent methods to automatically classify RFI signals. Data from di erent sources was cap- tured at the SKA site. Both Gaussian Mixture Model (GMM) and K-nearest neighbors (KNN) classi ers were used to analyse the data. Both performed adequately, with the KNN slightly outperforming the GMM. Di erent feature extraction methods were also investigated.National Research Foundatio