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 SEEK's SumThreshold implementation. We publish our U-Net software package on GitHub under GPLv3 license.
@article{arxiv.1609.09077,
title = {Radio frequency interference mitigation using deep convolutional neural networks},
author = {Joel Akeret and Chihway Chang and Aurelien Lucchi and Alexandre Refregier},
journal= {arXiv preprint arXiv:1609.09077},
year = {2017}
}
Comments
8 pages, 3 figures Published in Astronomy and Computing. The code is available at https://github.com/jakeret/tf_unet