RFI Identification Based On Deep-Learning]{A Robust RFI Identification For Radio Interferometry based on a Convolutional Neural Network

TL;DR

This paper presents a robust CNN-based method for identifying Radio Frequency Interference in radio interferometry data, demonstrating high accuracy and effectiveness on simulated and real observational data, improving over existing techniques.

Contribution

The paper introduces a novel CNN model for RFI identification that outperforms traditional methods and is validated on both simulated and real datasets from multiple radio telescopes.

Findings

AUC of 0.93 indicating high model accuracy

Effective RFI detection in real observational data

Model performance comparable or superior to AOFlagger

Abstract

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 datasets. One dataset was used for modeling, and the other two were used for validating the model's usability. The experimental results show that the Area Under the Curve (AUC) 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.