New Statistical Methods for Detecting Periodicity in Sparse Astronomical Data

AST-0507254 Rice该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，主要应用于旋转动力的伽马射线辐射，来自康普顿伽马射线天文台EGRET或即将推出的伽马射线大面积空间望远镜GLAST等系统记录的光子序列。 该方法是植根于半参数统计模型的假设检验，扩展到其他过程的泊松过程的周期性测试，以及测试非周期性偏差的均匀性。 目前的方法是有限的，他们的能力，占未知的频率漂移，而不实质上牺牲检测灵敏度，其计算需求是巨大的。 新的测试可能具有更高的灵敏度和显著提高的计算效率，并且在基础理论和有效的软件实现开发之后，EGRET数据库中的未识别源将作为第一个应用进行分析。沿着对学生和初级研究人员的跨学科培训，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.