CDS&E: AGNs Amidst the Data Deluge

在类星体被发现后的50年里，出现了一幅关于这些神秘物体的图片，其中类星体是位于大星系中心的超大质量黑洞。黑洞的质量可能是太阳的数百万到数十亿倍。正如这张图片所暗示的，类星体现象与被称为“活动星系核”的天体是相同的。它们都以巨大的光度为特征，以至于类星体经常比其宿主星系中所有数十亿颗恒星的光线更耀眼。类星体的亮度使人们可以在很远的距离上看到它，在这个距离上，物体是无法分辨的，也就是说，它的图像就像我们银河系中的一颗恒星。几十年来，通过各种不同的技术发现了类星体，每一种技术都倾向于选择具有有限特征的物体，如颜色或射电发射，这些特性可以将它与数量庞大的前景星系恒星区分开来。随着天文学进入“大数据”时代，它准备考虑海量数据集，这些数据集的数量远远超过之前考虑的任何数据集。具体地说，由PanSTARRS和大型天气观测望远镜制作的大型天空成像测量将包含数百万个类星体。该项目将开发筛选数据集的算法和技术，以提取类星体，以研究类星体群体性质随宇宙时间的演变。该项目将利用现代统计技术和现有成像调查中可用的颜色、可变性和天体测量信息的同时组合，创建迄今为止组装的最大类星体样本。具有这些参数的类星体选择目前是单独执行的，通常使用次优算法。只有通过同时考虑不同的数据类型和使用现代统计方法，下一代成像测量才能获得既最完整又没有污染的大类星体样本，从而实现其承诺并最大限度地提高其科学产出。这项工作处于天文学、统计学和计算机科学的十字路口，因为它处理的是用标准方法难以解决的海量天文数据集中的对象分类问题。这项工作将有可能扩展到下一代成像测量。这项工作的概念证明正在进行中，确定该项目构思良好，所需资源已到位。这些算法将在现有的调查中进行测试，以确认存在光谱数据。

In the fifty years since the discovery of quasars, a picture of these enigmatic objects has emerged wherein quasars are supermassive black holes residing at the centers of large galaxies. The black hole may have a mass of millions to billions times that of the sun. As this picture implies, the quasar phenomenon is one and the same as that of the objects referred to as "active galactic nuclei." All are characterized by enormous luminosity such that the quasar frequently outshines the light from all of the billions of stars in its host galaxy. The luminosity of the quasar allows it to be seen at very large distances, distances at which the object is unresolved, i.e., its image is like that of a star in our galaxy. Over the decades, quasars have been discovered by a variety of diverse techniques, each of which has a tendency to select objects of a limited range of characteristics such as color or radio emission, properties that can differentiate it from the huge number of foreground galactic stars. As astronomy enters the "Big Data" era, it is poised to consider massive data sets, the volumes of which are far larger than any considered previously. Specifically, large imaging surveys of the sky such as those produced by the PanSTARRS and Large Synoptic Survey Telescope will contain millions of quasars. This project will develop algorithms and techniques for sifting through the data sets to pull out the quasars for study of the evolution of the properties of the quasar population with cosmic time.The project will create the largest sample of quasar assembled to date using modern statistical techniques and the simultaneous combination of color, variability, and astrometric information available in existing imaging surveys. Quasar selection with these parameters is currently performed separately and generally with sub-optimal algorithms. It will only be through simultaneous consideration of heterogeneous data types and the use of modern statistical methods that next-generation imaging surveys will achieve large quasar samples that are both optimally complete and uncontaminated, enabling fulfillment of their promise and maximizing their science output. This work is at the crossroads of astronomy, statistics, and computer science, as it deals with the problem of object classification in massive astronomical data sets that would be intractable with standard methods. It will be possible to extend this work to the next-generation of imaging surveys. A proof of concept of this work is in hand, establishing that the project is both well-conceived and that the required resources are in place. The algorithms will be tested on existing surveys for which confirming spectroscopic data exist.