YOLO-CIANNA: 基于深度学习的射电数据星系检测 I. 一种应用于SKAO SDC1的新型YOLO启发式源检测方法
YOLO-CIANNA: Galaxy detection with deep learning in radio data. I. A new YOLO-inspired source detection method applied to the SKAO SDC1
AI总结：
针对SKA海量射电数据传统分析工具扩展性不足的问题，提出定制化YOLO-CIANNA深度学习源检测器，在SKAO SDC1模拟数据集上实现了检测纯度、源数量和表征精度的SOTA结果，计算效率高且有望迁移至实际观测数据。
摘要：
即将建成的平方公里阵列（Square Kilometer Array, SKA）将为天文仪器产生的数据量树立新标准，这可能会对现有广泛使用、但无法随数据规模充分扩展的数据分析工具构成挑战。本研究的目标是基于现代深度学习目标检测技术，为海量射电天文数据集开发一种新的源检测与表征方法。为此，我们旨在明确这类方法应用于天文数据时的具体优势与劣势。\n我们提出了YOLO-CIANNA，这是一款专门为天文数据集设计的高度定制化深度学习目标检测器。本文介绍了该方法，并阐述了为应对射电天文图像的特定挑战而引入的所有组件。随后，我们将该方法应用于SKA天文台科学数据挑战1（Science Data Challenge 1, SDC1）数据集的模拟二维连续谱图像，以此验证其性能。\n使用SDC1的评估指标，我们将挑战赛的冠军得分提升了+139%，较赛后唯一另一支参赛队伍的得分提升了+61%。我们的星表检测纯度达到94%，同时检测到的源数量比此前的最高分结果多40%至60%，并且表现出优异的表征精度。经过训练的模型还可在后处理阶段强制达到99%的纯度，此时检测到的源数量仍比其他最高分方法多10%至30%。该模型计算效率也很高，在单块GPU上的峰值预测速度可达每秒500张512×512像素的图像。\nYOLO-CIANNA在模拟SDC1数据集上取得了最先进的检测与表征结果，预计可很好地迁移至SKA先导望远镜的观测数据。
英文摘要：
The upcoming Square Kilometer Array (SKA) will set a new standard regarding data volume generated by an astronomical instrument, which is likely to challenge widely adopted data-analysis tools that scale inadequately with the data size. The aim of this study is to develop a new source detection and characterization method for massive radio astronomical datasets based on modern deep-learning object detection techniques. For this, we seek to identify the specific strengths and weaknesses of this type of approach when applied to astronomical data. We introduce YOLO-CIANNA, a highly customized deep-learning object detector designed specifically for astronomical datasets. In this paper, we present the method and describe all the elements introduced to address the specific challenges of radio astronomical images. We then demonstrate the capabilities of this method by applying it to simulated 2D continuum images from the SKA observatory Science Data Challenge 1 (SDC1) dataset. Using the SDC1 metric, we improve the challenge-winning score by +139% and the score of the only other post-challenge participation by +61%. Our catalog has a detection purity of 94% while detecting 40 to 60% more sources than previous top-score results, and exhibits strong characterization accuracy. The trained model can also be forced to reach 99% purity in post-process and still detect 10 to 30% more sources than the other top-score methods. It is also computationally efficient, with a peak prediction speed of 500 images of 512x512 pixels per second on a single GPU. YOLO-CIANNA achieves state-of-the-art detection and characterization results on the simulated SDC1 dataset and is expected to transfer well to observational data from SKA precursors.
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