Deep Learning improves identification of Radio Frequency Interference

TL;DR

This paper introduces R-Net, a deep convolutional ResNet model that significantly improves Radio Frequency Interference detection in radio astronomy, outperforming existing algorithms and demonstrating robustness across simulations and real data.

Contribution

The paper presents R-Net, a novel deep learning architecture that outperforms current RFI flagging methods and demonstrates effective transfer learning from simulated to real data.

Findings

R-Net achieves higher AUC, F1-score, and MCC than existing algorithms.

Transfer learning from simulated to real data significantly improves performance.

Phase information adds little value without calibration.

Abstract

Flagging of Radio Frequency Interference (RFI) is an increasingly important challenge in radio astronomy. We present R-Net, a deep convolutional ResNet architecture that significantly outperforms existing algorithms -- including the default MeerKAT RFI flagger, and deep U-Net architectures -- across all metrics including AUC, F1-score and MCC. We demonstrate the robustness of this improvement on both single dish and interferometric simulations and, using transfer learning, on real data. Our R-Net model's precision is approximately $90\%$ better than the current MeerKAT flagger at $80\%$ recall and has a 35\% higher F1-score with no additional performance cost. We further highlight the effectiveness of transfer learning from a model initially trained on simulated MeerKAT data and fine-tuned on real, human-flagged, KAT-7 data. Despite the wide differences in the nature of the two telescope arrays, the model achieves an AUC of 0.91, while the best model without transfer learning only reaches an AUC of 0.67. We consider the use of phase information in our models but find that without calibration the phase adds almost no extra information relative to amplitude data only. Our results strongly suggest that deep learning on simulations, boosted by transfer learning on real data, will likely play a key role in the future of RFI flagging of radio astronomy data.