Radio frequency interference mitigation using deep convolutional neural networks

28 Sep 2016 Joel Akeret Chihway Chang Aurelien Lucchi Alexandre Refregier

We propose a novel approach for mitigating radio frequency interference (RFI) signals in radio data using the latest advances in deep learning. We employ a special type of Convolutional Neural Network, the U-Net, that enables the classification of clean signal and RFI signatures in 2D time-ordered data acquired from a radio telescope... (read more)

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  • INSTRUMENTATION AND METHODS FOR ASTROPHYSICS