A Systematic Census of AGN Variability

AGN 变异性的系统普查

• 批准号: 2108402
• 负责人: Matthew Graham
• 金额: $ 63.81万
• 依托单位: California Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2108402&HistoricalAwards=false
• 关键词: Systematic; Census; AGN; Variability

Astronomical surveys are monitoring the night sky and looking for changes in the brightness of stars, galaxies, and asteroids. These surveys produce vast amounts of complex data, so machine learning techniques are being developed to analyze modern data sets. This team will use machine learning and time series techniques to identify active galactic nuclei and catalog the different ways in which their brightness can vary. Future sky surveys can draw on these results to sort out the different models that are developed to explain the variability. This work forms the basis for real-world student projects in data science summer schools. More broadly, the machine learning techniques could be applied to similar collections of time series in other fields, such as neuroscience, seismology, and climate science. Active galactic nuclei (AGN) form one of the largest populations of variable astronomical sources and play a key role in our understanding of accretion physics, relativistic physics, galaxy evolution, and large-scale structure. However, their variability is not simple and remains poorly studied in comparison to other more regular sources, partly due to a lack of appropriate statistical tools and methodologies. A new generation of sky surveys is enabling systematic studies of astrophysical variability, and more sophisticated analyses are now possible through machine learning that can reveal details of the nonlinear nature of the variability. The team will conduct a statistical study of AGN variability employing the Catalina Real-time Transient Survey and Zwicky Transient Facility data sets. These surveys have produced time series data for millions of known and potential AGN spanning almost twenty years with hundreds of observations per source. The team will construct a highly accurate and complete catalog of AGN based on continuous time autoencoders applied to multiband multiepoch photometry, thus combining both color and variability information. Using this, they will systematically identify AGN flares at optical and mid-IR wavelengths and define flare families based on characterizable properties. They will also use deep learning models to provide novel non-parametric discriminating features in the data set. This will establish the knowledge base and methodologies for AGN studies with LIGO and Rubin Observatory. Data products from the project will also form the basis for student projects in various data science and astronomy summer schools. This award addresses/advances the goals of the Windows on the Universe Big Idea.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

天文学调查正在监视夜空，并寻找恒星，星系和小行星的亮度变化。这些调查会产生大量的复杂数据，因此正在开发机器学习技术来分析现代数据集。该团队将使用机器学习和时间序列技术来识别活跃的银河核并分类其亮度不同的方式。未来的天空调查可以利用这些结果来整理出用于解释可变性的不同模型。这项工作构成了数据科学暑期学校的现实世界学生项目的基础。更广泛地说，机器学习技术可以应用于其他领域的时间序列的类似集合，例如神经科学，地震学和气候科学。主动银河核（AGN）构成了最大的可变天文来源人群之一，并在我们对吸积物理学，相对论物理学，银河系进化和大规模结构的理解中起着关键作用。但是，与其他更常规的来源相比，它们的变异性并不简单，部分原因是缺乏适当的统计工具和方法。新一代的天空调查正在实现对天体物理变异性的系统研究，现在可以通过机器学习进行更复杂的分析，从而可以揭示变异性非线性性质的细节。该团队将对使用Catalina实时瞬态调查和Zwicky瞬态设施数据集进行对AGN变异性的统计研究。这些调查已生成了数百万已知和潜在的AGN的时间序列数据，跨越了近20年，每个来源的观察值数百个。该团队将基于应用于多播多型多孔光度法的连续时间自动编码器的高度准确而完整的AGN目录，从而结合了颜色和可变性信息。使用此功能，他们将系统地识别光学和MID-IR波长处的AGN耀斑，并根据可特性的特性定义耀斑家族。他们还将使用深度学习模型在数据集中提供新颖的非参数歧视功能。这将建立使用Ligo和Rubin天文台的AGN研究的知识库和方法。该项目的数据产品还将构成各种数据科学和天文学暑期学校的学生项目的基础。该奖项旨在解决宇宙大想法的窗口的目标。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的审查标准，被认为值得通过评估来获得支持。

项目成果

The Type 1 and Type 2 AGN dichotomy according to their ZTF optical variability

根据 ZTF 光学可变性的 1 型和 2 型 AGN 二分法

• DOI: 10.1093/mnras/stac3174
• 发表时间: 2022
• 期刊: Monthly Notices of the Royal Astronomical Society
• 影响因子: 4.8
• 作者: López-Navas, E.;Arévalo, P.;Bernal, S.;Graham, Matthew J.;Hernández-García, L.;Lira, P.;Sánchez-Sáez, P.
• 通讯作者: Sánchez-Sáez, P.

Improving the selection of changing-look AGNs through multiwavelength photometric variability

• DOI: 10.1093/mnras/stad1893
• 发表时间: 2023-06
• 期刊: Monthly Notices of the Royal Astronomical Society
• 影响因子: 4.8
• 作者: E. L'opez-Navas;P. S'anchez-S'aez;P. Ar'evalo;S. Bernal;M. Graham;L. Hernandez-Garcia;D. Homan;M. Krumpe;G. Lamer;P. Lira;M. L. Mart'inez-Aldama;A. Merloni;S. R'ios;M. Salvato;D. Stern;D. Tub'in-Arenas

The WISE-2MASS Survey: Red Quasars Into the Radio Quiet Regime

• DOI: 10.3847/1538-4357/ac6bee
• 发表时间: 2022-04
• 期刊: The Astrophysical Journal
• 作者: E. Glikman;M. Lacy;S. LaMassa;C. Bradley;S. Djorgovski;T. Urrutia;E. Gates;M. Graham;M. Urry;I. Yoon

A Light in the Dark: Searching for Electromagnetic Counterparts to Black Hole–Black Hole Mergers in LIGO/Virgo O3 with the Zwicky Transient Facility

黑暗中的光明：寻找黑洞的电磁对应物——LIGO/Virgo O3 中的黑洞与 Zwicky 瞬态设施的合并

• DOI: 10.3847/1538-4357/aca480
• 发表时间: 2023
• 期刊: The Astrophysical Journal
• 作者: Graham, Matthew J.;McKernan, Barry;Ford, K. E. Saavik;Stern, Daniel;Djorgovski, S. G.;Coughlin, Michael;Burdge, Kevin B.;Bellm, Eric C.;Helou, George;Mahabal, Ashish A.
• 通讯作者: Mahabal, Ashish A.