CDS&E: AGNs Amidst the Data Deluge

在类星体发现后的五十年里，这些神秘物体的图片已经出现，其中类星体是居住在大型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”的物体是同一种现象。“所有这些都以巨大的光度为特征，以至于类星体经常超越其宿主星系中数十亿颗恒星的光线。 类星体的亮度允许它在非常远的距离被看到，在这个距离上物体是无法分辨的，即，它的图像就像我们银河系中的一颗星星。 几十年来，类星体已经被各种各样的技术发现，每一种技术都倾向于选择具有有限特征的物体，例如颜色或无线电发射，这些特性可以将其与大量的前景星系恒星区分开来。 随着天文学进入“大数据”时代，它准备考虑大量的数据集，其数量远远大于以前考虑的任何数据集。 具体来说，像PanSTARRS和大型综合巡天望远镜这样的大型天空成像巡天将包含数百万个类星体。 该项目将开发算法和技术，用于筛选数据集，从中提取类星体，研究类星体种群随宇宙时间的性质演变。该项目将利用现代统计技术，同时结合现有成像调查中提供的颜色、变化和天体测量信息，创建迄今为止最大的类星体样本。类星体的选择与这些参数目前是单独进行，一般与次优算法。只有通过同时考虑异构数据类型和使用现代统计方法，下一代成像调查才能获得最佳完整和未受污染的大型类星体样本，从而实现其承诺并最大限度地提高其科学产出。这项工作是天文学，统计学和计算机科学的十字路口，因为它涉及大量天文数据集的对象分类问题，这将是难以处理的标准方法。将有可能将这项工作扩展到下一代成像调查。这项工作的概念证明正在进行中，确定该项目构思良好，所需资源到位。 这些算法将在确认光谱数据存在的现有调查中进行测试。

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.