A Transformer-Conditioned Neural Fields Pipeline with Polar Coordinate Representation for Astronomical Radio Interferometric Data Reconstruction
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In radio astronomy, visibility data, which are measurements of wave signals from radio telescopes, are transformed into images for observation of distant celestial objects. However, these resultant images usually contain both real sources and artifacts, due to signal sparsity and other factors. One way to obtain cleaner images is to reconstruct samples into dense forms before imaging. Unfortunately, existing visibility reconstruction methods may miss some components of the frequency data, so blurred object edges and persistent artifacts remain in the images. Furthermore, the computation overhead is high on irregular visibility samples due to the data skew. To address these problems, we propose PolarRec, a reconstruction method for interferometric visibility data, which consists of a transformer-conditioned neural fields pipeline with a polar coordinate representation. This representation matches the way in which telescopes observe a celestial area as the Earth rotates. We further propose Radial Frequency Loss function, using radial coordinates in the polar coordinate system to correlate with the frequency information, to help reconstruct complete visibility. We also group visibility sample points by angular coordinates in the polar coordinate system, and use groups as the granularity for subsequent encoding with a Transformer encoder. Consequently, our method can capture the inherent characteristics of visibility data effectively and efficiently. Our experiments demonstrate that PolarRec markedly improves imaging results by faithfully reconstructing all frequency components in the visibility domain while significantly reducing the computation cost.
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