The upcoming Square Kilometer Array (SKA) telescope marks a significant step forward in radio astronomy, presenting new opportunities and challenges for data analysis. Traditional visual models pretrained on optical photography images may not perform optimally on radio interferometry images, which have distinct visual characteristics. Self-Supervised Learning (SSL) offers a promising approach to address this issue, leveraging the abundant unlabeled data in radio astronomy to train neural networks that learn useful representations from radio images. This study explores the application of SSL to radio astronomy, comparing the performance of SSL-trained models with that of traditional models pretrained on natural images, evaluating the importance of data curation for SSL, and assessing the potential benefits of self-supervision to different domain-specific radio astronomy datasets. Our results indicate that, SSL-trained models achieve significant improvements over the baseline in several downstream tasks, especially in the linear evaluation setting; when the entire backbone is fine-tuned, the benefits of SSL are less evident but still outperform pretraining. These findings suggest that SSL can play a valuable role in efficiently enhancing the analysis of radio astronomical data. The trained models and code is available at: \url{https://github.com/dr4thmos/solo-learn-radio}

翻译：即将建成的平方公里阵列（SKA）望远镜标志着射电天文学的重大进展，为数据分析带来了新的机遇与挑战。在光学摄影图像上预训练的传统视觉模型，可能无法在具有独特视觉特征的射电干涉测量图像上达到最佳性能。自监督学习为解决这一问题提供了一种前景广阔的方法，它利用射电天文学中丰富的未标记数据来训练神经网络，使其从射电图像中学习有用的表征。本研究探讨了自监督学习在射电天文学中的应用，比较了自监督学习训练模型与在自然图像上预训练的传统模型的性能，评估了数据整理对自监督学习的重要性，并评估了自监督学习对不同领域特定射电天文数据集的潜在益处。我们的结果表明，自监督学习训练的模型在多项下游任务中相比基线模型取得了显著改进，尤其是在线性评估设置下；当对整个骨干网络进行微调时，自监督学习的优势不那么明显，但仍优于预训练模型。这些发现表明，自监督学习可以在有效增强射电天文数据分析方面发挥重要作用。训练好的模型和代码可在以下网址获取：\url{https://github.com/dr4thmos/solo-learn-radio}