Radio frequency interference mitigation using deep convolutional neural networks

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. We train and assess the performance of this network using the HIDE & SEEK radio data simulation and processing packages, as well as early Science Verification data acquired with the 7m single-dish telescope at the Bleien Observatory. We find that our U Net implementation is showing competitive accuracy to classical RFI mitigation algorithms such as SEEKS SUMTHRESHOLD implementation. We publish our U Net software package on GitHub under GPLv3 license. (C) 2017 Elsevier B.V. All rights reserved.