• Corpus ID: 218685016

Deep Learning improves Radio Frequency Interference Classification

@article{Sadr2020DeepLI,
  title={Deep Learning improves Radio Frequency Interference Classification},
  author={Alireza Vafaei Sadr and Bruce A. Bassett and Nadeem Oozeer and Yabebal T. Fantaye and Chris Finlay},
  journal={arXiv: Instrumentation and Methods for Astrophysics},
  year={2020}
}
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… 

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