Predictive monitoring of aperiodic sources

许多对天空中亮度变化的物体的研究只关注周期性的变化，随着时间的变化是有规律的和重复的变化。然而，大多数可变天文来源是非周期性的，表现出不规则和不规则的变化。与周期源相比，它们的研究仍然很少，尽管它们可以在我们理解复杂的动态物理环境中发挥关键作用，从恒星到气体和尘埃流动，再到星系。这项工作利用美国国家科学基金会资助的两个项目的数据来研究非周期变化，这两个项目是卡特琳娜实时瞬变调查(CRTS)和新开始的兹维基瞬变设施(ZTF)。这是第一次对这些现象进行大规模的系统研究，也将是持续到下一个十年的决定性研究。它是数据密集型科学的一个很好的案例研究，将最先进的机器学习技术应用于真实数据。它将让学生接触尖端数据科学，其产品将为培训下一代如何处理大型天文数据的项目做出贡献。尽管大多数可变源是非周期性的，但人们对它们的理解很少，对于像类星体和年轻恒星这样的著名例子来说，许多基本的问题仍然存在，关于它们光学可变性背后的物理机制。新的天文观测使系统地研究可变性成为可能，并发现了许多新现象，包括亚帕秒分离的类星体双星、可归因于吸积盘中爆炸活动的微透镜的多年长耀斑，以及指示可变吸积率的变态源。这些极端行为应该很容易被现代稳健的统计方法发现。这项工作将从CRTS数据中创建具有新的非参数区分特征的生成性数据派生模型，并使用这些模型来预测非周期源的未来行为，然后可以使用ZTF和其他天气设施进行实时监测。以前的工作侧重于稳健的统计特征和确定极端变量类别。这将扩展到更一般的非周期源和更复杂的非参数生成模型。ZTF史无前例的天空报道与CRTS无与伦比的档案报道相结合，使这项研究成为权威研究。根据CRTS Open Data和ZTF警报政策，所有确定的过渡对象都将向社区发布。该奖项反映了NSF的法定使命，并已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。

Many studies of objects in the sky that vary in brightness look only at periodic variation, with a regular and repeating change with time. However, most variable astronomical sources are aperiodic, showing erratic and irregular changes. They remain poorly studied in comparison to periodic sources, even though they can play a key role in our understanding of complex dynamic physical environments, from stars, to gas and dust flows, to galaxies. This work studies aperiodic variability using the data from two NSF-supported projects, the Catalina Real-time Transient Survey (CRTS), and the newly begun Zwicky Transient Facility (ZTF). This is the first large-scale systematic study of these phenomena, and it will be the definitive study, well into the next decade. It is an excellent case study for data-intensive science, applying state-of-the-art machine learning techniques to real data. It will expose students to cutting-edge data science, and its products will contribute to projects that will train the next generation in how to handle big astronomical data.Even though the majority of variable sources are aperiodic, they are poorly understood, and for well-known examples like quasars and young stellar objects many fundamental questions remain about the physical mechanisms behind their optical variability. New sky surveys are enabling systematic studies of variability and discovering many new phenomena, including sub-parsec separated quasar binaries, multi-year long flares attributable to microlensing of explosive activity in the accretion disk, and changing-state sources indicative of variable accretion rates. These extreme behaviors should be easily discoverable with modern robust statistical methods. This work will create generative data-derived models with novel non-parametric discriminating features from CRTS data, and use the models to predict the future behavior of aperiodic sources, which can then be monitored in real-time using ZTF and other synoptic facilities. Prior work focused on robust statistical characterization and identifying extreme variable classes. This will be extended to more general aperiodic sources and more sophisticated non-parametric generative models. The unprecedented sky coverage of ZTF joins with the unequalled archival coverage of CRTS to make this the definitive study. In keeping with the CRTS Open Data and ZTF alert policies, all transitioning objects identified will be released to the community.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.