Computer Vision Methods for Frequency Analysis of RFI in Radio Astronomy Data
Natalia A. Schmid, Sasanka Katreddi, and Yechan Kweon

TL;DR
This paper introduces a transform-based RFI detection method using STFT and image segmentation that improves radio astronomy data cleaning without prior RFI knowledge.
Contribution
It develops a novel RFI mitigation technique employing STFT and image segmentation, outperforming traditional methods in weak and broadband interference scenarios.
Findings
Enhanced RFI suppression using STFT and segmentation.
Improved S/N ratio of pulsar signals after cleaning.
Method effective for weak, broadband, and non-stationary RFI.
Abstract
Radio Frequency Interference (RFI) increasingly contaminates the radio astronomy spectrum, often exceeding astronomical signal amplitudes by 50-70 dB. Reliable detection and mitigation are therefore essential for studies of faint transient phenomena such as pulsars and fast radio bursts (FRBs). Existing practical methods (including Spectral Kurtosis (SK), Median Absolute Deviation (MAD), and SumThreshold) perform well in many settings but depend on assumptions about the RFI environment and data statistics, limiting their effectiveness for weak, broadband, or non stationary interference. We develop a transform based RFI detection method that requires no prior knowledge of RFI origin or type. Using Green Bank Telescope (GBT) data containing PSR J1713+0747, with 4096 channels spanning 1.1-1.9 GHz and 5.12 micro second sampling, we apply a Short Time Fourier Transform (STFT) to each…
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