Categorize Radio Interference using component and temporal analysis

TL;DR

This paper introduces a novel classification method for radio frequency interference (RFI) using PCA and Fourier analysis, aiding in forensic analysis and source identification in radio astronomy.

Contribution

The study presents a new classifier that categorizes RFI types based on spectral features, enhancing RFI analysis and source tracking capabilities.

Findings

Successfully classifies RFI with F1 scores between 0.87 and 0.91

Identifies drifting periodic RFI detrimental to pulsar searches

Detects increased impulsive RFI near urban areas

Abstract

Radio frequency interference (RFI) is a significant challenge faced by today's radio astronomers. While most past efforts were devoted to cleaning the RFI from the data, we develop a novel method for categorizing and cataloguing RFI for forensic purpose. We present a classifier that categorizes RFI into different types based on features extracted using Principal Component Analysis (PCA) and Fourier analysis. The classifier can identify narrowband non-periodic RFI above 2 sigma, narrowband periodic RFI above 3 sigma, and wideband impulsive RFI above 5 sigma with F1 scores between 0.87 and 0.91 in simulation. This classifier could be used to identify the sources of RFI as well as to clean RFI contamination (particularly in pulsar search). In the long-term analysis of the categorized RFI, we found a special type of drifting periodic RFI that is detrimental to pulsar search. We also found evidences of an increased rate of impulsive RFI when the telescope is pointing toward the cities. These results demonstrate this classifier's potential as a forensic tool for RFI environment monitoring of radio telescopes.