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arXiv 1111.1631astro-ph.IMgr-qc
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寻找长持续时间引力波瞬变过程中噪声伪影的识别

Identification of noise artifacts in searches for long-duration gravitational-wave transients

Tanner Prestegard, Eric Thrane, Nelson L. Christensen, Michael W. Coughlin, Ben Hubbert, Shivaraj Kandhasamy, Evan MacAyeal, Vuk Mandic

摘要:

我们提出了一种算法,用于识别瞬态噪声伪影(毛刺)的互相关搜索长O(10秒)引力波瞬态。该算法利用每个检测器中的自动功率作为表现良好的高斯噪声(可能包括引力波信号)和毛刺之间的干扰。我们用蒙特卡罗噪声和LIGO S5科学运行的时移数据测试了该算法,发现它能有效地去除很大一部分毛刺,同时保留绝大多数(99.6%)数据。使用一个吸积盘不稳定性信号模型,我们估计,该算法被意外触发的比率小于10 ^-5%的现实信号,甚至小于3%的异常响亮的信号。我们的结论是,该算法是一个安全和有效的方法清洗互相关数据用于搜索长引力波瞬变。

英文摘要:

We present an algorithm for the identification of transient noise artifacts (glitches) in cross-correlation searches for long O(10s) gravitational-wave transients. The algorithm utilizes the auto-power in each detector as a discriminator between well-behaved Gaussian noise (possibly including a gravitational-wave signal) and glitches. We test the algorithm with both Monte Carlo noise and time-shifted data from the LIGO S5 science run and find that it is effective at removing a significant fraction of glitches while keeping the vast majority (99.6%) of the data. Using an accretion disk instability signal model, we estimate that the algorithm is accidentally triggered at a rate of less than 10^-5% by realistic signals, and less than 3% even for exceptionally loud signals. We conclude that the algorithm is a safe and effective method for cleaning the cross-correlation data used in searches for long gravitational-wave transients.

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