SEI: Collaborative Research: Discovering Unexpected Planets and Other Astronomical Oddities

SEI：合作研究：发现意想不到的行星和其他天文奇异现象

• 批准号: 0713273
• 负责人: Charles Alcock
• 金额: $ 18.26万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-15 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0713273&HistoricalAwards=false
• 关键词: SEI; Collaborative; Research; Discovering; Unexpected

IIS - 0713273Alcock, CharlesHarvard UniversityCollaborative Research: Discovering Unexpected Planets and Other Astronomical OdditiesThis project seeks to provide automated methods for analyzing sky surveys to detect extra-solar planets via anomaly and novelty detection methods. Extremely large astronomical synoptic surveys will soon monitor much of the sky regularly, detecting vast numbers of interesting, variable astronomical objects. The objective of this proposal is to develop the tools necessary to exploit these new data in order advance discovery. The proposed work will explore the scientific analysis and modeling of massive datasets of light-curves (66 million light-curves now available, growing to 100 billion in a decade) from a broad range of perspectives. The unique complications posed by the data in this domain will drive research for data mining techniques to allow these analyses. A comprehensive framework of models and their relationships with the data will be developed that readily separates two kinds of interesting objects: (1) Rare objects that reveal special insights about the models; and (2) Potential truly novel objects that cannot be described by the models. New computationally tractable time series algorithms for novelty detection will be developed for these purposes.

IIS -0713273阿尔科克，查尔斯哈佛大学合作研究：发现意外的行星和其他天文学上的奇怪这个项目旨在提供自动化的方法来分析天空调查，以检测太阳系外行星通过异常和新奇的检测方法。非常大的天文天气调查将很快定期监测天空的大部分，探测大量有趣的，可变的天文物体。这项建议的目的是开发利用这些新数据所需的工具，以便提前发现。 拟议的工作将从广泛的角度探索光变曲线的大规模数据集的科学分析和建模（现在有6600万条光变曲线，十年内将增长到1000亿条）。该领域中的数据所带来的独特复杂性将推动数据挖掘技术的研究，以实现这些分析。 将开发一个全面的模型框架及其与数据的关系，容易区分两种有趣的对象：（1）罕见的对象，揭示了关于模型的特殊见解;（2）潜在的真正新颖的对象，不能被模型描述。新的计算易处理的时间序列算法的新奇检测将开发用于这些目的。