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，并对其亮度变化的不同方式进行分类。未来的天空调查可以利用这些结果来整理出不同的模型，这些模型是为了解释这种变化而开发的。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)是世界上最大的可变天文源群之一，在我们对吸积物理、相对论物理、星系演化和大尺度结构的理解中起着关键作用。然而，它们的可变性并不简单，与其他更经常的来源相比，对它们的研究仍然很少，部分原因是缺乏适当的统计工具和方法。新一代天文观测使对天体物理可变性的系统研究成为可能，现在可以通过机器学习进行更复杂的分析，从而揭示可变性的非线性性质的细节。该团队将利用Catalina实时瞬变调查和兹维基瞬变设施数据集对AGN的可变性进行统计研究。这些调查产生了跨越近20年的数百万已知和潜在活动星系核的时间序列数据，每个源有数百次观测。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，构建高度准确和完整的活动星系核目录，从而结合颜色和可变性信息。利用这一点，他们将系统地识别光学和中红外波长的活动星系核耀斑，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颖的非参数区分特征。这将为与LIGO和鲁宾天文台进行AGN研究建立知识库和方法论。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。这一奖项致力于/推进宇宙之窗的目标。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.