使用深度卷积神经网络的射频干扰抑制
Radio frequency interference mitigation using deep convolutional neural networks
摘要：
我们提出了一种新的方法，利用深度学习的最新进展来减轻无线电数据中的射频干扰（RFI）信号。我们采用了一种特殊类型的卷积神经网络，即U-网络，它可以对从射电望远镜获得的二维时间顺序数据中的干净信号和RFI签名进行分类。我们训练和评估这个网络的性能，使用HIDE & SEEK无线电数据模拟和处理包，以及早期的科学验证数据获得的7米单碟望远镜在布林天文台。我们发现，我们的U-Net实现显示出竞争力的准确性，经典的RFI缓解算法，如SEEK的SumThreshold实现。我们在GitHub上发布U-Net软件包，并遵循GPLv 3许可证。
英文摘要：
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.
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