标题： 使用深度学习优化稀疏RFI预测 显示英文标题

标题： Optimizing Sparse RFI Prediction using Deep Learning

摘要： 射频干扰（RFI）是无线电望远镜中一个持续存在的限制因素，即使在最偏远的观测地点也是如此。 在致力于保留最大灵敏度并减少再电离时期研究的污染时，识别和消除RFI尤其重要。 除了改进的RFI识别外，我们还必须考虑RFI识别算法的计算效率，因为像氢再电离时期阵列这样的射电干涉仪阵列的接收器数量正在增加。 为了解决这个问题，我们提出了一种深度全卷积神经网络（DFCN），它全面利用了干涉数据，其中幅度和相位信息被联合用于识别RFI。 我们使用包含模拟HERA可见性的数据训练网络，这些数据包含模拟的RFI，从而生成了一个已知的“真实”数据集，用于评估各种RFI算法的准确性。 DFCN模型的评估是在67个天线构建的HERA-67观测数据上进行的，并实现了每小时每GPU 1.6$\times 10^{5}$HERA时间有序的1024通道可见性数据吞吐量。 我们确定，相对于仅使用幅度的网络，引入可见性相位增加了重要的相邻时间和频率上下文，这有助于区分RFI和非RFI。 在预测时引入相位可实现召回率为0.81，精确率为0.58，以及$F_{2}$得分为0.75，应用于我们的HERA-67观测数据。 显示英文摘要

摘要： Radio Frequency Interference (RFI) is an ever-present limiting factor among radio telescopes even in the most remote observing locations. When looking to retain the maximum amount of sensitivity and reduce contamination for Epoch of Reionization studies, the identification and removal of RFI is especially important. In addition to improved RFI identification, we must also take into account computational efficiency of the RFI-Identification algorithm as radio interferometer arrays such as the Hydrogen Epoch of Reionization Array grow larger in number of receivers. To address this, we present a Deep Fully Convolutional Neural Network (DFCN) that is comprehensive in its use of interferometric data, where both amplitude and phase information are used jointly for identifying RFI. We train the network using simulated HERA visibilities containing mock RFI, yielding a known "ground truth" dataset for evaluating the accuracy of various RFI algorithms. Evaluation of the DFCN model is performed on observations from the 67 dish build-out, HERA-67, and achieves a data throughput of 1.6$\times 10^{5}$ HERA time-ordered 1024 channeled visibilities per hour per GPU. We determine that relative to an amplitude only network including visibility phase adds important adjacent time-frequency context which increases discrimination between RFI and Non-RFI. The inclusion of phase when predicting achieves a Recall of 0.81, Precision of 0.58, and $F_{2}$ score of 0.75 as applied to our HERA-67 observations.