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.

使用深度学习技术，提出了一种用于射电数据中减轻射电频率干扰信号（RFI）的新方法，通过一种特殊的卷积神经网络，该网络可以对来自射电望远镜获得的 2D 有序时间数据中的清洁信号和 RFI 信号进行分类。作者实现的 U-Net 得到了与已有的 RFM 算法相媲美的结果。