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

合作研究：类星体变异性系统方法的新见解

• 批准号: 1518308
• 负责人: Stanislav Djorgovski
• 金额: $ 28.97万
• 依托单位: California Institute of Technology
• 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=1518308&HistoricalAwards=false
• 关键词: Collaborative; Research; New; Insights; Systematic

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整个生命的一生中如何形成和发展。 这种理解对于发现我们的宇宙如何发展并洞悉我们自己银河系的起源是必要的。 理解星系形成和进化的一个重要方面是研究类星体和其他活跃的银河核。 它们本身也是有趣的天体物理现象，并充当相对论物理学的探测。 他们与宿主星系共同进化并追踪宇宙结构的演变。 但是，大多数类星体发现的方法都是基于其宽带光谱分布的特性，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存在非热发射的样品。 可变性为类星体发现提供了独立于频谱的方法。 尽管根据个人或几个对象进行了大量研究，但基于可变性的类星体调查迄今已限于最多有几千个对象和/或时间分辨率差的小型天空区域。 这项针头可变性的研究采用了Catalina实时瞬态调查（CRTS）数据集，该数据集覆盖了大于9年的基线，覆盖了约80％的天空。目前，其5亿个对象数据集拥有约25万个已知的类星体，500,000个光度测量值候选者和估计有1,000,000个新变异性的类星体。 这将构成迄今为止最大的类星体数据集。 为了与CRT开放数据策略保持一致，该项目中确定的所有类星体（和其他分类对象）都将释放给社区。 这将构成类星体和更多一般可变性研究的主要新资源。 要使用的统计方法也适用于任何不规则采样的时间序列，这些时间序列与机器学习技术的组合是用于数据密集型科学的案例研究。该项目是与主要的本科机构的合作，并直接增强了两种大学生的茎教育。 与加州理工学院数据驱动发现中心（CD3）的科学家合作，他们将接触到数据科学领域的尖端技术，包括对数据挖掘的高水平使用以及从这些大数据集中提取有意义的结果。 该项目的数据产品还构成了由美国智利联合资助的La Serena数据科学学校的学生项目的基础，培训下一代用于处理大天文数据的应用工具。特别是，该项目将重点介绍（i）类Quasar变异性特征的相关性，尤其是特征性的时间表，尤其是物理参数，诸如Luminosity，Black hole sosics，例如Black Hole sosevers，例如Black hole smosity和Edding the Edding tary cartio; （ii）定期可变性作为超质量黑洞二进制的可能证据； （iii）变异性是年轻的灰尘笼罩的红色类星体的晦涩的探针； （iv）量化可变性的波长依赖性，以改善类星体选择并限制不同物理过程的不同模型。 该研究将采用现代的统计技术，可以与不规则采样的Gappy时间序列自然合作，而无需重新注射或平滑。 结合机器学习方法，这些方法将产生最佳的基于合奏的结果，例如用于类星体选择的新可变性 - 多色素方法。 该项目将是对LSST时代的光学类星体变异性的重要研究。 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，并且在时间分辨率方面（观测数 /基线的数量）更好。 它还将大大增加可能已知的高可能性类星体候选者的数量，尤其是在SDS不涵盖的天空地区。