Segmenting RFI Using Meta’s Segment Anything Model

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Radio Frequency Interference (RFI), radio signals from electronics, permeates through astronomical observations using radio telescopes. The interference results in lower sensitivity and higher noise when present. Existing methods, such as TFCrop and RFlag, flag RFI to prevent the contamination of observations. However, these approaches are partially automatic and require manual input to flag RFI. We explore artificial intelligence methods using Meta’s Segment Anything Model (SAM) and its parameter space in segmenting RFI. We have developed an open-source pipeline called SamRFI, which segments and flags RFI from measurement sets. We trained SAM’s masking weights from Hugging Face to fine-tune the model using Very Large Array data and synthetic waterfall plots injected with RFI. We define a metric, calcquality, to measure the performance of our models based on sensitivity and overflagging. Retrained SAM models identify RFI structure and allow for further exploration in RFI segmentation.