Radio telescopes produce visibility data about celestial objects, but these
data are sparse and noisy. As a result, images created on raw visibility data
are of low quality. Recent studies have used deep learning models to
reconstruct visibility data to get cleaner images. However, these methods rely
on a substantial amount of labeled training data, which requires significant
labeling effort from radio astronomers. Addressing this challenge, we propose
VisRec, a model-agnostic semi-supervised learning approach to the
reconstruction of visibility data. Specifically, VisRec consists of both a
supervised learning module and an unsupervised learning module. In the
supervised learning module, we introduce a set of data augmentation functions
to produce diverse training examples. In comparison, the unsupervised learning
module in VisRec augments unlabeled data and uses reconstructions from
non-augmented visibility data as pseudo-labels for training. This hybrid
approach allows VisRec to effectively leverage both labeled and unlabeled data.
This way, VisRec performs well even when labeled data is scarce. Our evaluation
results show that VisRec outperforms all baseline methods in reconstruction
quality, robustness against common observation perturbation, and
generalizability to different telescope configurations.

通过深度学习模型以及半监督学习方法来重建天线可见度数据以提高图像质量。

VisRec: 用于射电干涉数据重建的半监督方法

VisRec: A Semi-Supervised Approach to Radio Interferometric Data  Reconstruction

This work proposes to reduce visibility data volume using a
baseline-dependent lossy compression technique that preserves smearing at the
edges of the field-of-view. We exploit the relation of the rank of a matrix and
the fact that a low-rank approximation can describe the raw visibility data as
a sum of basic components where each basic component corresponds to a specific
Fourier component of the sky distribution. As such, the entire visibility data
is represented as a collection of data matrices from baselines, instead of a
single tensor. The proposed methods are formulated as follows: provided a large
dataset of the entire visibility data; the first algorithm, named $simple~SVD$
projects the data into a regular sampling space of rank$-r$ data matrices. In
this space, the data for all the baselines has the same rank, which makes the
compression factor equal across all baselines. The second algorithm, named
$BDSVD$ projects the data into an irregular sampling space of rank$-r_{pq}$
data matrices. The subscript $pq$ indicates that the rank of the data matrix
varies across baselines $pq$, which makes the compression factor
baseline-dependent. MeerKAT and the European Very Long Baseline Interferometry
Network are used as reference telescopes to evaluate and compare the
performance of the proposed methods against traditional methods, such as
traditional averaging and baseline-dependent averaging (BDA). For the same
spatial resolution threshold, both $simple~SVD$ and $BDSVD$ show effective
compression by two-orders of magnitude higher than traditional averaging and
BDA. At the same space-saving rate, there is no decrease in spatial resolution
and there is a reduction in the noise variance in the data which improves the
S/N to over $1.5$ dB at the edges of the field-of-view.

提出了一种基于基线相关的有损压缩技术，可以将能见度数据进行压缩，并且利用矩阵秩的关系以及低秩逼近的方法来描述原始数据，并将其表示为基线数据矩阵的集合，其中每个基本分量分别对应于天空分布的特定傅里叶分量。该方法可以有效压缩数据并且在空间分辨率和噪声降低方面有所改进，其中 MeerKAT 和欧洲长基线干涉仪网络被用于评估和比较所提出的方法。