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arXiv 2208.03372gr-qcastro-ph.HE
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参数化引力波偏振

Parametrizing gravitational-wave polarizations

Maximiliano Isi

AI总结:

本文综述引力波偏振建模形式体系,厘清三种偏振角定义,比较完全偏振模式的参数化及其雅可比变换,并指出振幅参数化的偏振偏差,服务于无模型、连续波及铃宕分析。

摘要:

我们综述了引力波(GW)偏振建模所依据的形式体系,以及用于定义这些偏振的坐标参考系。在此过程中,我们阐明了“偏振角”的概念,并识别出三种在概念上不同的定义。我们描述了这些定义如何相互关联,以及它们如何在GW数据分析实践中产生,并详细解释了已成为LIGO-Virgo标准的相关约定。此外,我们证明任何GW信号都可以表示为椭圆(即完全偏振)态的叠加,并考察了这些基本态的性质和可能的参数化方式。我们讨论了完全偏振模式的多种常见参数化,并计算了联系这些参数化的坐标变换的雅可比矩阵。这使我们能够考察每种参数化对不同应用的适用性,包括无模型或半模型信号重建。我们指出,直接用plus和cross模式振幅进行参数化的分析往往会隐含地偏好高信号功率,并偏好沿预定义方向的线偏振波;这使其不适合针对正对或背向源的目标分析,因为这些源往往是圆偏振的。我们讨论了替代参数化,其应用扩展到连续波、铃宕研究以及诸如BayesWave之类的无模型分析。代码和附加材料可在https://github.com/maxisi/gwpols获取。

英文摘要:

We review the formalism underlying the modeling of gravitational wave (GW) polarizations, and the coordinate frames used to define them. In the process, we clarify the notion of "polarization angle" and identify three conceptually distinct definitions. We describe how those are related and how they arise in the practice of GW data analysis, explaining in detail the relevant conventions that have become the LIGO-Virgo standard. Furthermore, we show that any GW signal can be expressed as a superposition of elliptical (i.e., fully-polarized) states, and examine the properties and possible parametrizations of such elementary states. We discuss a variety of common parametrizations for fully-polarized modes, and compute Jacobians for the coordinate transformations relating them. This allows us to examine the suitability of each parametrization for different applications, including unmodeled or semimodeled signal reconstructions. We point out that analyses parametrized directly in terms of the plus and cross mode amplitudes will tend to implicitly favor high signal power, and to prefer linearly-polarized waves along a predefined direction; this makes them suboptimal for targeting face-on or face-off sources, which will tend to be circularly polarized. We discuss alternative parametrizations, with applications extending to continuous waves, ringdown studies, and unmodeled analyses like BayesWave. Code and additional material are made available in https://github.com/maxisi/gwpols.

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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