一种用于红外暗云定位的半自动化计算方法：红外暗云目录
A Semi-Automated Computational Approach for Infrared Dark Cloud Localization: A Catalog of Infrared Dark Clouds
AI总结：
针对红外暗云（IRDCs）特征复杂难以可靠识别的问题，提出一种结合轮廓查找与卷积神经网络（CNNs）的半自动化方法，在 Spitzer 银道面巡天数据中搜寻 IRDCs 并构建了包含更广区域的新目录。
摘要：
过去十年间，计算机视觉领域已高度成熟，其诸多方法与技术可应用于天文学。一个典型例子是在大型成像巡天中搜寻目标天体，尤其是当难以明确指定所寻天体特征时。我们开发了一种利用轮廓查找和卷积神经网络（CNNs）在 Spitzer 银道面巡天数据中搜寻红外暗云（IRDCs）的方法。IRDCs 的大小、形状、取向和光深各异，且通常位于具有来自分子云和恒星形成复杂辐射的区域附近，这使得 IRDCs 难以被可靠识别。在缺乏辐射而非由前景 IRDC 产生的区域可能出现假阳性。我们实现的轮廓查找算法找到了拼接图中的大多数闭合图形，并制定了规则在让 CNNs 分析前过滤掉部分假阳性。该方法被应用于银道面巡天的 Spitzer 数据，我们构建了一个 IRDCs 目录，其中包含了早期巡天未涵盖的银道面其他部分。
英文摘要：
The field of computer vision has greatly matured in the past decade, and many of the methods and techniques can be useful for astronomical applications. One example is in searching large imaging surveys for objects of interest, especially when it is difficult to specify the characteristics of the objects being searched for. We have developed a method using contour finding and convolution neural networks (CNNs) to search for Infrared Dark Clouds (IRDCs) in the Spitzer Galactic plane survey data. IRDCs can vary in size, shape, orientation, and optical depth, and are often located near regions with complex emission from molecular clouds and star formation, which can make the IRDCs difficult to reliably identify. False positives can occur in regions where emission is absent, rather than from a foreground IRDC. The contour finding algorithm we implemented found most closed figures in the mosaic and we developed rules to filter out some of the false positive before allowing the CNNs to analyze them. The method was applied to the Spitzer data in the Galactic plane surveys, and we have constructed a catalog of IRDCs which includes additional parts of the Galactic plane that were not included in earlier surveys.
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。
