4.7 Article

Fast gravitational wave radiometry using data folding

期刊

PHYSICAL REVIEW D
卷 92, 期 2, 页码 -

出版社

AMER PHYSICAL SOC
DOI: 10.1103/PhysRevD.92.022003

关键词

-

资金

  1. Council of Scientific and Industrial Research (CSIR), India
  2. Science and Engineering Research Board (SERB), India [SR/FTP/PS-030/2012]

向作者/读者索取更多资源

Gravitational waves (GWs) from the early universe and unresolved astrophysical sources are expected to create a stochastic GW background (SGWB). The GW radiometer algorithm is well suited to probe such a background using data from ground-based laser interferometric detectors. Radiometer analysis can be performed in different bases, e.g., isotropic, pixel or spherical harmonic. Each of these analyses possesses a common temporal symmetry which we exploit here to fold the whole data set for every detector pair, typically a few hundred to a thousand days of data, to only one sidereal day, without any compromise in precision. We develop the algebra and a software pipeline needed to fold data, accounting for the effect of overlapping windows and nonstationary noise. We implement this on LIGO's fifth science run data and validate it by performing a standard anisotropic SGWB search on both folded and unfolded data. Folded data not only leads to orders of magnitude reduction in computation cost, but it results in a conveniently small data volume of few gigabytes, making it possible to perform an actual analysis on a personal computer, as well as easy movement of data. A few important analyses, yet unaccomplished due to computational limitations, will now become feasible. Folded data, being independent of the radiometer basis, will also be useful in reducing processing redundancies in multiple searches and provide a common ground for mutual consistency checks. Most importantly, folded data will allow vast amount of experimentation with existing searches and provide substantial help in developing new strategies to find unknown sources.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据