A Systematic Census of AGN Variability

天文观测正在监测夜空，寻找恒星、星系和小行星亮度的变化。这些调查产生了大量复杂的数据，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动星系核，并对它们的亮度变化的不同方式进行编目。未来的天空调查可以利用这些结果来整理出不同的模型来解释这种变化。这项工作构成了数据科学暑期学校真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类似时间序列集合，如神经科学，地震学和气候科学。活动星系核（AGN）是最大的变量天文源之一，在我们理解吸积物理，相对论物理，星系演化和大尺度结构中起着关键作用。然而，其可变性并不简单，与其他更经常的来源相比，对其研究仍然不足，部分原因是缺乏适当的统计工具和方法。新一代的巡天观测使人们能够系统地研究天体物理学的变化，现在可以通过机器学习进行更复杂的分析，揭示变化的非线性性质的细节。该小组将利用卡塔利纳实时瞬变测量和兹威基瞬变设施数据集对活动星系核的变化进行统计研究。这些调查产生了数百万已知的和潜在的活动星系核的时间序列数据，跨越近20年，每个源有数百个观测。该团队将基于应用于多波段多历元光度测量的连续时间自动编码器构建一个高度准确和完整的AGN目录，从而结合颜色和变化信息。利用这一点，他们将在光学和中红外波长系统地识别活动星系核耀斑，并根据可表征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的非参数识别特征。这将为LIGO和鲁宾天文台的活动星系核研究建立知识基础和方法。该项目的数据产品也将成为各种数据科学和天文学暑期学校学生项目的基础。该奖项旨在解决/推进Windows on the Universe Big Idea的目标。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.