Identification of RF Interference in Astronomical Observations Using Weakly Supervised Machine Learning Classifiers

Abstract

Radio frequency interference (RFI) is a major concern for passive radioastronomical observations. There is a great interest within the wireless and radioastronomy communities to identify in real time man-made RFI in the vicinity of a telescope. This paper proposes the use of weakly supervised machine learning (ML) techniques to detect the presence of RFI in captured astronomical scans. Weakly supervised training is particularly appropriate when only a small subset of captured data is labeled, as is the case with many radioastronomical datasets. Our study is based on scans obtained from the Arizona Radio Observatory (ARO) at Kitt Peak, Arizona. We rely on the experience of astronomical engineers to label ten 20 MHz channels of a small fraction of the captured scans as "clean" or "dirty". The remaining channels of 4 GHz of the observed spectrum are unlabeled. We first use human-labeled data as ground truth and train two ML classifiers in a supervised manner: a Convolutional Neural Networks - Bidirectional Long Short Term Memory (CNN-BiLSTM) classifier and a Deep CNN classifier. For the unlabeled channels, a semi-supervised technique is adopted, whereby the unlabeled data is first fed to the trained supervised classifier and the outputs with high confidence are assigned pseudo labels. These pseudo-labeled data are further used to train a semi-supervised classifier. To test the performance of the semi-supervised technique, the two classifiers are considered again. We observe test accuracies of 94.55% and 93.69% respectively under weakly supervised training.