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arXiv 2101.08264gr-qc
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在第三代引力波探测器中识别II型强透镜引力波图像

Identifying Type II Strongly Lensed Gravitational-Wave Images in Third-Generation Gravitational-Wave Detectors

Yijun Wang, Rico K. L. Lo, Alvin K. Y. Li, Yanbei Chen

摘要:

强引力透镜是一种引力波(GW)传播效应,它会影响所推断的GW源参数和宇宙学环境。识别强透镜GW图像是具有挑战性的,因为波形振幅放大率随着源固有质量和红移的偏移而退化。然而,即使在几何光学极限下,II型强透镜图像也不能完全与I型(或无透镜)波形模板匹配,特别是在大的二进制质量比和轨道倾角的情况下。我们建议使用这种不匹配来区分个别II型图像。利用宇宙探测器,爱因斯坦望远镜和LIGO旅行者计划的噪声谱,我们表明,一个显着的比例II型图像可以区分从unlensed源,给出足够的SNR($\sim 30$)。通过对GW源群和透镜群的模型分析,我们预测CE、ET和LIGO Voyager的可探测II型图像的透镜GW源年探测率分别为172.2、118.2和27.4。在这些可检测事件中,33.1%、7.3%和0.22%将通过其具有大于10的对数贝叶斯因子的II型图像来区分。我们的结论是,这种可区分的事件可能会出现在第三代探测器目录中,我们的策略将显着补充现有的强透镜搜索策略。

英文摘要:

Strong gravitational lensing is a gravitational wave (GW) propagation effect that influences the inferred GW source parameters and the cosmological environment. Identifying strongly-lensed GW images is challenging as waveform amplitude magnification is degenerate with a shift in the source intrinsic mass and redshift. However, even in the geometric-optics limit, type II strongly-lensed images cannot be fully matched by type I (or unlensed) waveform templates, especially with large binary mass ratios and orbital inclination angles. We propose to use this mismatch to distinguish individual type II images. Using planned noise spectra of Cosmic Explorer, Einstein Telescope and LIGO Voyager, we show that a significant fraction of type II images can be distinguished from unlensed sources, given sufficient SNR ($\sim 30$). Incorporating models on GW source population and lens population, we predict that the yearly detection rate of lensed GW sources with detectable type II images is 172.2, 118.2 and 27.4 for CE, ET and LIGO Voyager, respectively. Among these detectable events, 33.1%, 7.3% and 0.22% will be distinguishable via their type II images with a log Bayes factor larger than 10. We conclude that such distinguishable events are likely to appear in the third-generation detector catalog; our strategy will significantly supplement existing strong lensing search strategies.

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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