CDS&E: AGNs Amidst the Data Deluge

CDS

• 批准号: 1411773
• 负责人: Gordon Richards
• 金额: $ 17.95万
• 依托单位: Drexel University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-15 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1411773&HistoricalAwards=false
• 关键词: CDS& AGNs; Amidst; Data; Deluge

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.

在类星体发现后的50年里，一幅关于这些神秘物体的图景出现了，其中类星体是位于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。正如这幅图所暗示的那样，类星体现象与被称为“活动星系核”的物体是同一种现象。所有类星体的特征都是巨大的亮度，以至于类星体经常比其宿主星系中数十亿颗恒星发出的光都要亮。类星体的亮度使得它可以在非常远的距离上被观测到，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。在过去的几十年里，人们通过各种各样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范围的物体，比如颜色或射电发射，这些特征可以将其与大量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，其数量远远大于之前考虑的任何数据集。具体来说，由PanSTARRS和大型综合巡天望远镜（large synotic Survey Telescope）制作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来筛选数据集，以提取类星体，以研究类星体种群特性随宇宙时间的演变。该项目将创建迄今为止最大的类星体样本，使用现代统计技术，同时结合现有成像调查中可用的颜色、变异性和天体测量信息。类星体的这些参数选择目前是单独进行的，通常采用次优算法。只有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既完整又未受污染的大型类星体样本，从而实现其承诺并最大化其科学产出。这项工作处于天文学、统计学和计算机科学的十字路口，因为它处理大量天文数据集中的对象分类问题，这将是标准方法难以处理的。将这项工作扩展到下一代成像调查是可能的。这项工作的概念证明已经到手了，可以确定项目是经过精心构思的，并且所需的资源已经到位。这些算法将在现有的调查中进行测试，这些调查已经有了确认光谱数据。

项目成果

QUASAR CLASSIFICATION USING COLOR AND VARIABILITY

使用颜色和变异性进行类星体分类

• DOI: 10.1088/0004-637x/811/2/95
• 发表时间: 2015
• 期刊: The Astrophysical Journal
• 作者: Peters, Christina M.;Richards, Gordon T.;Myers, Adam D.;Strauss, Michael A.;Schmidt, Kasper B.;Ivezic´, Željko;Ross, Nicholas P.;MacLeod, Chelsea L.;Riegel, Ryan
• 通讯作者: Riegel, Ryan