Kavli Affiliate: Feng Wang
| First 5 Authors: Ruoqi Wang, Zhuoyang Chen, Jiayi Zhu, Qiong Luo, Feng Wang
| Summary:
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 reconstruction methods often miss some
components of visibility in frequency domain, 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 transformer-encoder-conditioned
reconstruction pipeline with visibility samples converted into the polar
coordinate representation. This representation matches the way in which radio
telescopes observe a celestial area as the Earth rotates. As a result,
visibility samples distribute in the polar system more uniformly than in the
Cartesian space. Therefore, we propose to use radial distance in the loss
function, to help reconstruct complete visibility effectively. Also, we group
visibility samples by their polar angles and propose a group-based encoding
scheme to improve the efficiency. 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 in visibility data encoding. We believe this high-quality and
high-efficiency imaging of PolarRec will better facilitate astronomers to
conduct their research.
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