Supervised Neural Networks for RFI Flagging
This work addresses RFI detection for radio astronomy data processing, but it is incremental as it applies existing neural network methods to a specific dataset using ground truth from an existing technique.
The paper tackled the problem of detecting radio frequency interference (RFI) in post-correlation, post-calibration time/frequency data using neural networks, achieving a Recall of 0.69, Precision of 0.83, and an F1-Score of 0.75 with a single-layer fully connected network.
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.