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Categorize radio interference using component and temporal analysis
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AI
DOI：10.1093/mnras/stac963.png)
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Abstract
En 中文
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 [defined as F1 = (2 center dot recall x precision)/(recall + precision)] 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 pieces of evidence of an increased rate of impulsive RFI when the telescope is pointing towards the cities. These results demonstrate this classifier's potential as a forensic tool for RFI environment monitoring of radio telescopes.
Keywords:
methods: data analysis
techniques: miscellaneous
catalogues
pulsars: general
Journal
IF:
4.8
Papers:
7.0W
Citations:
25.0W