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 does affect RFI for the sake of this work a reduced dataset in post-calibration is used .
Two machine learning approaches for flagging real measurement data are demonstrated using the existing RFI flagging technique AOFlagger as a ground truth .
It is shown that a single layer fully connect network can be trained using each time/frequency sample individually with the magnitude and phase of each polarization and Stokes visibilities as features .
This method was able to predict a Boolean flag map for each baseline to a high degree of accuracy achieving a Recall of 0.69 and Precision of 0.83 and an F1-Score of 0.75 .

The second approach utilizes a convolutional neural network ( CNN ) implemented in the U-Net architecture , shown in literature to work effectively on simulated radio data .
In this work the architecture trained on real data results in a Recall , Precision and F1-Score 0.84 , 0.91 , 0.87 respectfully .

This work seeks to investigate the application of supervised learning when trained on a ground truth from existing flagging techniques , the results of which inherently contain false positives .
In order for a fair comparison to be made the data is imaged using CASA ’ s CLEAN algorithm and the U-Net and NN ’ s flagging results allow for 5 and 6 additional radio sources to be identified respectively .