New Statistical Methods for Detecting Periodicity in Sparse Astronomical Data

本项目涉及在低计数或稀疏观测制度下测试天文数据的周期性，主要应用于旋转驱动的脉冲星的伽马射线发射，来自康普顿伽马射线天文台的ERET或即将推出的伽马射线大面积空间望远镜等系统记录的光子序列。该方法植根于对半参数统计模型的假设检验，将对泊松过程周期性的检验也扩展到其他过程，并对非周期偏离一致性进行检验。目前的方法在不显著牺牲检测灵敏度的情况下解决未知频率漂移的能力受到限制，并且它们的计算需求是巨大的。新的测试可能具有更高的灵敏度和极大的计算效率，在基础理论和有效的软件实现开发出来后，白鹭数据库中的不明来源将作为第一个应用程序进行分析。随着学生和初级研究人员的跨学科培训，极大量的周期和非周期变量现象意味着对天文学的潜在影响是相当大的。

AST-0507254RiceThis project is concerned with testing for periodicity of astronomical data in a low-count or sparse observation regime, with primary application to gamma-ray emissions from rotation-powered pulsars, from the sequence of photons recorded by a system such as EGRET on the Compton Gamma-Ray Observatory, or the forthcoming Gamma Ray Large Area Space Telescope, GLAST. The methodology is rooted in hypothesis testing for semi-parametric statistical models, extending tests for periodicity of Poisson processes to other processes as well, and also to testing for non-periodic deviations from uniformity. Current approaches are limited by their ability to account for unknown frequency drift without substantially sacrificing detection sensitivity, and their computational demands are enormous. The new tests are potentially of greater sensitivity and dramatically increased computational efficiency, and after the underlying theory and an efficient software implementation are developed, unidentified sources in the EGRET database will be analyzed as the first application.Along with the inter-disciplinary training of students and junior researchers, the extremely large number of periodic and non-periodic variable phenomena means that the potential impact on astronomy is considerable.