论文标题
RFI标记的监督神经网络
Supervised Neural Networks for RFI Flagging
论文作者
论文摘要
基于神经网络（NN）的方法应用于射频干扰（RFI）的检测，并在相关后，校准后时间/频率数据中。虽然为了为这项工作起见，校准对RFI进行了影响。使用现有的RFI标记技术Aoflagger作为地面图，证明了两种机器学习方法，以标记真实的测量数据。结果表明，单层完全连接网络可以使用每个时间/频率样本单独训练，并以每个极化和stokesvisibilities的大小和相位作为特征。该方法能够预测每个基线的Aboolean标志图，以高度准确性，召回0.69，精度为0.83，ANF1得分为0.75。
Neural network (NN) based methods are applied to the detection of radio frequency interference (RFI) in post-correlation,post-calibration time/frequency data. While calibration doesaffect RFI for the sake of this work a reduced dataset inpost-calibration is used. Two machine learning approachesfor flagging real measurement data are demonstrated usingthe existing RFI flagging technique AOFlagger as a groundtruth. It is shown that a single layer fully connects networkcan be trained using each time/frequency sample individuallywith the magnitude and phase of each polarization and Stokesvisibilities as features. This method was able to predict aBoolean flag map for each baseline to a high degree of accuracy achieving a Recall of 0.69 and Precision of 0.83 and anF1-Score of 0.75.
