论文标题
二进制中子星的高准确性高质量模拟及其与现有波形模型的比较
High-accuracy high-mass ratio simulations for binary neutron stars and their comparison to existing waveform models
论文作者
论文摘要
随后的高级重力波检测器网络的观察运行可能会为我们提供对二元中子星系的各种引力波的观察。为了准确解释这些检测,我们需要可靠的重力波模型。为了测试并指出如何改善现有模型,我们为具有质量比$ Q $ Q $ Q $ Q $ Q $ Q $ Q $ Q $ Q $ Q $ Q $ = $ 1.50 $,$ 1.75 $,$ 1.75 $,$ 2.00 $和总重力质量和总重力质量$ M = 2.27亿$ = 270万\ ODOT $的四种不同的物理设置进行了一组高分辨率的数值模拟。每种配置都使用五种不同的分辨率模拟,以允许进行适当的错误评估。总体而言,我们发现大约2订单收敛的$(2,2)$,但同时也是亚域$(2,1)$,$(3,3)$,$(4,4)$模式,而通常,融合订单的质量比率略有下降。我们的模拟使我们能够验证波形模型,在该模型中,我们发现最先进的模型和数据之间的一致性一致,并证明当前用于二进制黑孔波形建模的较高模式的比例关系也适用于潮汐贡献。最后,我们还测试了当前的NRTIDAL模型是否描述了潮汐效应,这是高质量比率系统的有效描述。我们希望我们的仿真结果可用于进一步改进和测试波形模型,以准备下一次观察跑步。
The subsequent observing runs of the advanced gravitational-wave detector network will likely provide us with various gravitational-wave observations of binary neutron star systems. For an accurate interpretation of these detections, we need reliable gravitational-wave models. To test and to point out how existing models could be improved, we perform a set of high-resolution numerical-relativity simulations for four different physical setups with mass ratios $q$ = $1.25$, $1.50$, $1.75$, $2.00$, and total gravitational mass $M = 2.7M_\odot$ . Each configuration is simulated with five different resolutions to allow a proper error assessment. Overall, we find approximately 2nd order converging results for the dominant $(2,2)$, but also subdominant $(2,1)$, $(3,3)$, $(4,4)$ modes, while, generally, the convergence order reduces slightly for an increasing mass ratio. Our simulations allow us to validate waveform models, where we find generally good agreement between state-of-the-art models and our data, and to prove that scaling relations for higher modes currently employed for binary black hole waveform modeling also apply for the tidal contribution. Finally, we also test if the current NRTidal model to describe tidal effects is a valid description for high-mass ratio systems. We hope that our simulation results can be used to further improve and test waveform models in preparation for the next observing runs.