Using Machine Learning for the detection of Radio Frequency Interference

Radio Astronomy, by its very nature, detects extremely faint cosmic radio signals and is therefore very susceptible to Radio Frequency Interference (RFI). We present some initial results of our work to identify RFI using a Machine Learning (ML) based approach. The data is taken from VLBI observations data from three well separated observatories in Australia: ATCA, Parkes and Mopra; and we work with the 2-bit data directly from the telescopes. Our approach uses a Generative Adversarial Network (GAN) and an autoencoder to perform unsupervised machine learning on the data.