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arXiv 1504.07802astro-ph.HEastro-ph.IMgr-qc
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用合并双星黑洞的引力波观测鉴别紧致双星布居合成模型

Distinguishing compact binary population synthesis models using gravitational-wave observations of coalescing binary black holes

Simon Stevenson, Frank Ohme, Stephen Fairhurst

摘要:

包含中子星或黑洞的致密双星的合并是先进的地面激光干涉仪引力波探测器最有希望的信号之一,预计在未来几年内将首次直接探测到。二元聚结的速率和组分质量的分布是高度不确定的,人口合成模型预测了一个广泛的合理值。人口合成模型中的参数约束较差,对应于在大质量双星演化的各个阶段对天体物理学知之甚少,双星是双中子星和双黑洞系统的祖先。这些影响包括超新星反冲速度、普通包层演化的能量学参数和恒星风的强度。通过引力波观测多个双黑洞系统将使我们能够推断导致其形成的天体物理机制的细节。在这里,我们模拟引力波观测一系列人口综合模型,包括已知的选择偏差,测量误差和宇宙学的影响。我们比较了不同模型的预测,并表明我们将能够区分它们与先进的LIGO和Virgo探测器早期运行的观测(或缺乏观测)。这将使我们能够缩小二元演化模型的大参数空间。

英文摘要:

The coalescence of compact binaries containing neutron stars or black holes is one of the most promising signals for advanced ground-based laser interferometer gravitational-wave detectors, with the first direct detections expected over the next few years. The rate of binary coalescences and the distribution of component masses is highly uncertain, and population synthesis models predict a wide range of plausible values. Poorly constrained parameters in population synthesis models correspond to poorly understood astrophysics at various stages in the evolution of massive binary stars, the progenitors of binary neutron star and binary black hole systems. These include effects such as supernova kick velocities, parameters governing the energetics of common envelope evolution and the strength of stellar winds. Observing multiple binary black hole systems through gravitational waves will allow us to infer details of the astrophysical mechanisms that lead to their formation. Here we simulate gravitational-wave observations from a series of population synthesis models including the effects of known selection biases, measurement errors and cosmology. We compare the predictions arising from different models and show that we will be able to distinguish between them with observations (or the lack of them) from the early runs of the advanced LIGO and Virgo detectors. This will allow us to narrow down the large parameter space for binary evolution models.

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