http://arxiv.org/abs/1609.09077
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 data collected at the Bleien Observatory. We find that our U-Net implementation can outperform classical RFI mitigation algorithms such as SEEK’s SumThreshold implementation. We publish our U-Net software package on GitHub under GPLv3 license.
J. Akeret, C. Chang, A. Lucchi, et. al.
Fri, 30 Sep 16
30/75
Comments: 5 pages, 3 figures Submitted to Astronomy and Computing. The code is available at this https URL
You must be logged in to post a comment.