Collaborative Research: CDS&E: Optimizing discovery with multi-epoch photometric survey data

目前和即将进行的多历元数字巡天正在使天文学发生革命性变化，它以前所未有的广度和灵敏度探测时域，产生关于可变和瞬态物体的大量信息。 该项目将开发新的统计和机器学习方法和软件，以利用多时期成像测量数据改进天体物理物体的探测、定性和分类。 这种科学赋能技术将在各种地基和天基调查环境中发挥作用。 新的分类法还将有助于优化有限的后续行动资源的分配。 所有算法都将在公开的开源软件中实现。 该团队包括天文学家，应用数学家和统计学家，该项目需要跨学科的创新，并将加强他们的伙伴关系。 这项工作将培养一名应用数学专业的研究生，以解决天文学中的数据科学问题，并包括支持培训各种年轻天文学家掌握先进的统计和机器学习方法。物体检测、表征和分类是从调查数据中提取科学的基础。 本项目的主要工作是开发基于多波段光变曲线形状的可变和瞬态物体分类新算法。 第二种方法将使用贝叶斯神经网络，使用改进的先验和改进的解决方案空间探索。 伴随组件解决最佳规格和源似然函数的使用，开发源不确定性的更准确的表示，努力实现暗淡，可变对象的最佳检测，并进行宇宙人口的人口建模。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

Current and forthcoming multi-epoch digital sky surveys are revolutionizing astronomy by probing the time domain with unprecedented breadth and sensitivity, producing vast amounts of information on variable and transient objects. This project will develop new statistical and machine learning methods and software to improve the detection, characterization, and classification of astrophysical objects using multi-epoch imaging survey data. This science-enabling technology will be useful in diverse ground- and space-based survey settings. New classifiers will also help to optimize the allocation of limited follow-up resources. All algorithms will be implemented in public, open-source software. The team includes astronomers, applied mathematicians, and statisticians, and this project requires innovation across disciplines and will enhance their partnership. The work will train a graduate student in applied math to work on data science problems in astronomy, and includes support to train a diverse population of young astronomers in advanced statistics and machine learning methods.Object detection, characterization, and classification are fundamental to extracting science from survey data. The main work of this project is to develop new algorithms for classifying variable and transient objects based on the shapes of their multiband light curves. A second approach will use Bayesian neural networks, using improved priors and improved solution space exploration. Companion components address optimal specification and use of source likelihood functions, developing more accurate representations of source uncertainty, working towards the optimal detection of dim, variable objects, and doing demographic modeling of cosmic populations.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.