天体普查的人工智能：最新技术与最古老的科学相遇
Artificial intelligence for celestial object census: the latest technology meets the oldest science
摘要：
使用现代望远镜进行的大型调查正在产生尺寸和质量呈指数级增长的图像。通过视觉识别识别生成的图像中的对象是耗时且劳动密集型的，而对提取的无线电源进行分类更具挑战性。为了应对这些挑战，我们开发了一种基于深度学习的射电源检测器，名为\textsc{HeTu}，它能够以自动化的方式快速识别和分类紧凑和扩展的射电源。\textsc{HeTu}基于残差网络（ResNet）和特征金字塔网络（FPN）的组合。我们将射电源分为四类，根据他们的形态。对训练图像进行人工标记，并采用数据增强方法解决不同类别之间的数据不平衡问题。\textsc{HeTu}自动定位图像中的射电源，并将其分配到四个类别中的一个。在测试数据集上的实验表明，每幅图像的平均运算时间为5.4ms，对紧凑点源的精度为99.4%，对双瓣源的精度为98.1%。我们应用\textsc{HeTu}从银河系和银河系河外全对象默奇森宽场阵列（GLEAM）调查项目获得的图像。与GLEAM中使用的源查找软件相比，超过96.9%的\textsc{HeTu}检测到的紧凑源是匹配的。我们还探测并分类了2，298个$5σ$以上的扩展源（包括Fanaroff-Riley I型和II型源以及核心喷流源）。扩展源的交叉匹配率大于97%，表明了\textsc{HeTu}在扩展源识别中的优良性能。\textsc{HeTu}为射电源的寻找和分类提供了一种有效的工具，并可应用于其他科学领域。
英文摘要：
Large surveys using modern telescopes are producing images that are increasing exponentially in size and quality. Identifying objects in the generated images by visual recognition is time-consuming and labor-intensive, while classifying the extracted radio sources is even more challenging. To address these challenges, we develop a deep learning-based radio source detector, named \textsc{HeTu}, which is capable of rapidly identifying and classifying radio sources in an automated manner for both compact and extended radio sources. \textsc{HeTu} is based on a combination of a residual network (ResNet) and feature pyramid network (FPN). We classify radio sources into four classes based on their morphology. The training images are manually labeled and data augmentation methods are applied to solve the data imbalance between the different classes. \textsc{HeTu} automatically locates the radio sources in the images and assigns them to one of the four classes. The experiment on the testing dataset shows an average operation time of 5.4 millisecond per image and a precision of 99.4\% for compact point-like sources and 98.1\% for double-lobe sources. We applied \textsc{HeTu} to the images obtained from the GaLactic and the Galactic Extragalactic All-Object Murchison Wide-field Array (GLEAM) survey project. More than 96.9\% of the \textsc{HeTu}-detected compact sources are matched compared to the source finding software used in the GLEAM. We also detected and classified 2,298 extended sources (including Fanaroff-Riley type I and II sources, and core-jet sources) above $5σ$. The cross-matching rates of extended sources are higher than 97\%, showing excellent performance of \textsc{HeTu} in identifying extended radio sources. \textsc{HeTu} provides an efficient tool for radio source finding and classification and can be applied to other scientific fields.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
