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

As radio telescopes increase in sensitivity and flexibility, so do their
complexity and data-rates. For this reason automated system health management
approaches are becoming increasingly critical to ensure nominal telescope
operations. We propose a new machine learning anomaly detection framework for
classifying both commonly occurring anomalies in radio telescopes as well as
detecting unknown rare anomalies that the system has potentially not yet seen.
To evaluate our method, we present a dataset consisting of 7050
autocorrelation-based spectrograms from the Low Frequency Array (LOFAR)
telescope and assign 10 different labels relating to the system-wide anomalies
from the perspective of telescope operators. This includes electronic failures,
miscalibration, solar storms, network and compute hardware errors among many
more. We demonstrate how a novel Self Supervised Learning (SSL) paradigm, that
utilises both context prediction and reconstruction losses, is effective in
learning normal behaviour of the LOFAR telescope. We present the Radio
Observatory Anomaly Detector (ROAD), a framework that combines both SSL-based
anomaly detection and a supervised classification, thereby enabling both
classification of both commonly occurring anomalies and detection of unseen
anomalies. We demonstrate that our system is real-time in the context of the
LOFAR data processing pipeline, requiring <1ms to process a single spectrogram.
Furthermore, ROAD obtains an anomaly detection F-2 score of 0.92 while
maintaining a false positive rate of ~2\%, as well as a mean per-class
classification F-2 score 0.89, outperforming other related works.

本文提出一种基于自监督学习的异常检测框架，应用于广泛的射电望远镜系统，能够在实时处理中，有效地分类常见异常及检测未知异常。通过为 Low Frequency Array（LOFAR）望远镜提供的数据集验证，该方法取得了高效且有效的结果，表现优于其他相关作品。

机器学习驱动的无线电天文光谱图中异常探测之路

The ROAD to discovery: machine learning-driven anomaly detection in  radio astronomy spectrograms

Imaging the Universe during the first hundreds of millions of years remains
one of the exciting challenges facing modern cosmology. Observations of the
redshifted 21 cm line of atomic hydrogen offer the potential of opening a new
window into this epoch. This would transform our understanding of the formation
of the first stars and galaxies and of the thermal history of the Universe. A
new generation of radio telescopes is being constructed for this purpose with
the first results starting to trickle in. In this review, we detail the physics
that governs the 21 cm signal and describe what might be learnt from upcoming
observations. We also generalize our discussion to intensity mapping of other
atomic and molecular lines.

用具有红移的 21 厘米原子氢谱线成像宇宙是现代宇宙学中令人兴奋的挑战之一。本文综述了控制 21 厘米信号的物理学，并描述了未来观测可能学到的内容，同时将讨论推广到其他原子和分子线的强度映射。