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arXiv 1505.08084gr-qcastro-ph.HEastro-ph.IM
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什么时候引力波信号是随机的?

When is a gravitational-wave signal stochastic?

Neil J. Cornish, Joseph D. Romano

摘要:

我们讨论了引力波背景检测的背景下,贝叶斯推断,并提出了一个实际的定义,它意味着什么信号被认为是随机的-即,贝叶斯证据有利于一个确定性的信号模型的随机信号模型。如果高斯信号模型是有利的,则信号可以进一步被分类为高斯随机的。在我们的分析中,我们使用贝叶斯模型选择几个信号和噪声模型的模拟数据组成的不相关的高斯探测器噪声加上叠加的正弦信号从天体物理人口的引力波源之间进行选择。为了简单起见,我们认为共同定位和共同对齐的检测器与白检测器噪声,但该方法可以扩展到更现实的检测器配置和功率谱。我们观察到的总趋势是,一个确定性的模型是有利于小的源数,一个非高斯随机模型是首选的中间源数,和高斯随机模型是首选的大的源数。然而,在各个信号实现之间存在非常大的变化,导致三种状态之间的模糊边界。我们发现,在大多数情况下,一个混合的,跨维的模型,包括一个确定性的信号模型为个别明亮的来源和高斯随机信号模型为其余的混乱背景优于所有其他模型。

英文摘要:

We discuss the detection of gravitational-wave backgrounds in the context of Bayesian inference and suggest a practical definition of what it means for a signal to be considered stochastic---namely, that the Bayesian evidence favors a stochastic signal model over a deterministic signal model. A signal can further be classified as Gaussian-stochastic if a Gaussian signal model is favored. In our analysis we use Bayesian model selection to choose between several signal and noise models for simulated data consisting of uncorrelated Gaussian detector noise plus a superposition of sinusoidal signals from an astrophysical population of gravitational-wave sources. For simplicity, we consider co-located and co-aligned detectors with white detector noise, but the method can be extended to more realistic detector configurations and power spectra. The general trend we observe is that a deterministic model is favored for small source numbers, a non-Gaussian stochastic model is preferred for intermediate source numbers, and a Gaussian stochastic model is preferred for large source numbers. However, there is very large variation between individual signal realizations, leading to fuzzy boundaries between the three regimes. We find that a hybrid, trans-dimensional model comprised of a deterministic signal model for individual bright sources and a Gaussian-stochastic signal model for the remaining confusion background outperforms all other models in most instances.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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