Collaborative Research: RUI: New Insights from a Systematic Approach to Quasar Variability

合作研究：RUI：类星体变异性系统方法的新见解

• 批准号: 1517510
• 负责人: Eilat Glikman
• 金额: $ 4.92万
• 依托单位: Middlebury College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-15 至 2018-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1517510&HistoricalAwards=false
• 关键词: Collaborative; Research; RUI; New; Insights

A longstanding problem in astrophysics is to understand how galaxies form and develop throughout their lifetimes. Such understanding is necessary to uncover how our Universe evolved and to gain insight into the origin of our own Milky Way Galaxy. One important aspect of understanding galaxy formation and evolution is to study quasars and other active galactic nuclei. They are also interesting astrophysical phenomena in their own right and serve as a probe of relativistic physics. They co-evolve with their host galaxies and trace the evolution of cosmic structure. However, most of the methods for quasar discovery are based on the properties of their broadband spectral energy distributions, and nearly all known quasars and quasar candidates come from samples that use some type of flux ratios or just the presence of a non-thermal emission. Variability offers a spectrum-independent method for quasar discovery. Although variability has been much studied on the basis of individual or a few objects, variability-based quasar surveys have so far been limited to small dedicated regions of sky with at most a few thousand objects and/or poor time resolution. This study of quasar variability employs the Catalina Real-time Transient Survey (CRTS) data set, which covers about 80\% of the sky over a baseline greater than 9 years. Its 500 million object data set currently holds about 250,000 known quasars, 500,000 photometric quasar candidates and an estimated 1,000,000 new variability-selected quasars. This will form the largest quasar data set to date. In keeping with the CRTS Open Data policy, all quasars (and other classified objects) identified in this project will be released to the community. This will form a major new resource for both quasar and more general variability studies. The statistical methods to be used are also applicable to any irregular-sampled time series, and the combination of these with machine-learning techniques is a case study for data-intensive science.This project is a collaboration with a primarily undergraduate institution and directly enhances the STEM education of two undergraduates. Working with scientists at the Center for Data-Driven Discovery (CD3) at Caltech, they will be exposed to cutting-edge techniques in data science, including high level usage of data mining and extracting meaningful results from these large data sets. Data products from this project also form the basis for student projects at the joint US-Chile-funded La Serena School for Data Science, training the next generation in applied tools for handling big astronomical data.In particular, this project will focus on (i) the correlation of quasar variability features, particularly characteristic timescales, with physical parameters, such as luminosity, black hole mass, and the Eddington ratio; (ii) periodic variability as possible evidence for supermassive black hole binaries; (iii) variability as a probe of obscuration in young dust-enshrouded red quasars; and (iv) quantifying wavelength dependencies of variability to improve quasar selection and constrain different models of physical processes. The study will employ modern statistical techniques that can work naturally with irregularly-sampled gappy time series without the need for reprojection or smoothing. In combination with machine-learning methods, these will produce optimal ensemble-based results, such as new variability-polychromatic methods for quasar selection. This project will be a key study on optical quasar variability well into the LSST era. It is at least two orders of magnitude larger than any previous study in terms of sky coverage and number of quasars and an order of magnitude better in terms of time resolution (number of observations / baseline). It will also substantially increase the number of high likelihood quasar candidates known, particularly in the regions of the sky not covered by SDSS.

天体物理学的一个长期问题是了解星系在其一生中是如何形成和发展的。 这样的理解对于揭示我们的宇宙是如何演化的以及深入了解我们银河系的起源是必要的。 了解星系形成和演化的一个重要方面是研究类星体和其他活动星系核。 它们本身也是有趣的天体物理现象，并可以作为相对论物理学的探测器。 它们与宿主星系共同演化，并追踪宇宙结构的演化。 然而，大多数发现类星体的方法都是基于它们的宽带光谱能量分布的特性，几乎所有已知的类星体和类星体候选者都来自使用某种类型的通量比或仅仅存在非热发射的样本。 变率为类星体的发现提供了一种与光谱无关的方法。 虽然已经在单个或几个物体的基础上对变率进行了大量研究，但基于变率的类星体调查迄今为止仅限于天空中最多几千个物体和/或时间分辨率差的小区域。 这项类星体变化的研究采用了卡塔利纳实时瞬态巡天（CRTS）数据集，该数据集在超过9年的基线上覆盖了大约80%的天空。它的5亿个天体数据集目前拥有大约25万个已知类星体，50万个测光类星体候选者和估计100万个新的可变选择类星体。 这将形成迄今为止最大的类星体数据集。 根据CRTS开放数据政策，该项目中识别的所有类星体（和其他分类对象）将向社区发布。 这将为类星体和更普遍的变率研究提供一个重要的新资源。 所使用的统计方法也适用于任何不规则采样的时间序列，这些方法与机器学习技术的结合是数据密集型科学的案例研究。该项目是与一所以本科为主的机构合作，直接加强了两名本科生的STEM教育。 与加州理工学院数据驱动发现中心（CD 3）的科学家合作，他们将接触到数据科学的尖端技术，包括数据挖掘的高层次使用和从这些大型数据集中提取有意义的结果。 该项目的数据产品也构成了美国-智利联合资助的拉塞雷纳数据科学学院学生项目的基础，培训下一代处理大量天文数据的应用工具，特别是该项目将侧重于（i）类星体可变性特征，特别是特征时标与物理参数的相关性，如光度，黑洞质量和爱丁顿比;（ii）周期性变化作为超大质量黑洞双星的可能证据;（iii）变化作为年轻尘埃笼罩的红类星体的遮蔽探测器;（iv）量化变化的波长依赖性，以改善类星体的选择和约束不同的物理过程模型。 这项研究将采用现代统计技术，可以自然地处理不规则采样的间隙时间序列，而不需要重新投影或平滑。 结合机器学习方法，这些将产生最佳的基于集合的结果，例如用于类星体选择的新的可变多色方法。 该项目将是进入LSST时代的光学类星体变化的关键研究。 就天空覆盖范围和类星体数量而言，它至少比以前的任何研究都大两个数量级，就时间分辨率（观测/基线数量）而言，它比以前的任何研究都大一个数量级。 它还将大大增加已知的高可能性类星体候选者的数量，特别是在SDSS未覆盖的天空区域。