论文标题
基于深度学习的RFI识别] {基于卷积神经网络的无线电干涉法的鲁棒RFI识别
RFI Identification Based On Deep-Learning]{A Robust RFI Identification For Radio Interferometry based on a Convolutional Neural Network
论文作者
论文摘要
新一代无线电干涉仪的快速发展（例如平方公里阵列（SKA））为天文学研究打开了前所未有的机会。但是，通信技术和其他人类活动的人为射频干扰（RFI）严重影响了观察数据的保真度。它还显着降低了望远镜的灵敏度。我们提出了一个强大的卷积神经网络（CNN）模型，以基于机器学习方法识别RFI。我们在SKA1-LOW的仿真数据上叠加了RFI，以构建三个可见性函数数据集。一个数据集用于建模，另外两个用于验证模型的可用性。实验结果表明，曲线下的面积（AUC）以令人满意的精度和精度达到0.93。然后，我们通过从Lofar和Meerkat的实际观察数据中识别RFI进一步研究了模型的有效性。结果表明该模型的性能很好。总体有效性与Aoflagger软件相当，并在某些情况下对现有方法提供了改进。
The rapid development of new generation radio interferometers such as the Square Kilometer Array (SKA) has opened up unprecedented opportunities for astronomical research. However, anthropogenic Radio Frequency Interference (RFI) from communication technologies and other human activities severely affects the fidelity of observational data. It also significantly reduces the sensitivity of the telescopes. We proposed a robust Convolutional Neural Network (CNN) model to identify RFI based on machine learning methods. We overlaid RFI on the simulation data of SKA1-LOW to construct three visibility function datasets. One dataset was used for modeling, and the other two were used for validating the model's usability. The experimental results show that the Area Under the Curve (AUC) reaches 0.93, with satisfactory accuracy and precision. We then further investigated the effectiveness of the model by identifying the RFI in the actual observational data from LOFAR and MeerKAT. The results show that the model performs well. The overall effectiveness is comparable to AOFlagger software and provides an improvement over existing methods in some instances.
