Learning to detect radio frequency interference in radio astronomy without seeing it

Radio frequency interference (RFI) corrupts astronomical measurements, thus affecting the performance of radio telescopes. To address this problem, supervised-segmentation models have been proposed as candidate solutions to RFI detection. However, the unavailability of large labelled data sets, due to the prohibitive cost of annotating, makes these solutions unusable. To solve these shortcomings, we focus on the inverse problem: training models on only uncontaminated emissions, thereby learning to discriminate RFI from all known astronomical signals and system noise. We use nearest latent neighbours - an algorithm that utilizes both the reconstructions and latent distances to the nearest neighbours in the latent space of generative autoencoding models for novelty detection. The uncontaminated regions are selected using weak labels in the form of RFI flags (generated by classical RFI flagging methods) available from most radio astronomical data archives at no additional cost. We evaluate performance on two independent data sets, one simulated from the Hydrogen Epoch of Reionization Array (HERA) telescope and the other consisting of real observations from the Low-Frequency Array (LOFAR) telescope. Additionally, we provide a small expert-labelled LOFAR data set (i.e. strong labels) for evaluation of our and other methods. Performance is measured using the area under the receiver operating characteristic (AUROC), area under precision-recall curve (AUPRC), and the maximum F1-score for a fixed threshold. For the simulated HERA data set, we outperform the current state of the art across all metrics. For the LOFAR data set, our algorithm offers both a 4 per cent increase in AUROC and AUPRC at the cost of increasing the false negative rate, but without any manual labelling.