论文标题
红外乌云本地化的半自动化计算方法：红外乌云目录
A Semi-Automated Computational Approach for Infrared Dark Cloud Localization: A Catalog of Infrared Dark Clouds
论文作者
论文摘要
在过去的十年中，计算机视觉领域已经大大成熟，许多方法和技术对于天文应用都有用。一个例子是搜索大型成像调查是否感兴趣的对象，尤其是在难以指定要搜索的对象的特征时。我们已经使用轮廓发现和卷积神经网络（CNN）开发了一种方法，以在Spitzer银河平面调查数据中搜索红外乌云（IRDC）。 IRDC的大小，形状，方向和光学深度可能变化，并且通常位于分子云和恒星形成的区域附近，这可能使IRDC难以可靠地识别。假阳性可能发生在缺乏发射的区域，而不是来自前景IRDC的区域。我们实施的轮廓发现算法在马赛克中发现了最封闭的数字，并制定了规则以滤除一些假阳性，然后再允许CNN分析它们。该方法应用于银河平面调查中的Spitzer数据，我们构建了IRDC的目录，其中包括银河平面的其他部分，这些部分未包含在早期的调查中。
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.
