A robust RFI identification for radio interferometry based on a convolutional neural network

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 data sets. One data set was used for modelling, and the other two were used for validating the model's usability. The experimental results show that the area under the curve 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.