论文标题
稳定的传质和恒星风对于重力波源的形成
Importance of stable mass transfer and stellar winds for the formation of gravitational wave sources
论文作者
论文摘要
大量重力波(GW)检测揭示了合并黑洞二元二进制人群的特性,但是如何形成这种系统仍在进行激烈的争论。了解恒星物理学在可观察到的GW种群上的烙印将阐明我们如何使用重力波数据以及其他观察结果来限制对大型二进制文件的发展鲜明的演变。我们对具有种群合成代码SEBA的经典孤立二进制形成通道进行了一项参数研究,以研究合并二进制黑洞人群的敏感性对与传质和恒星风的不确定性相关的不确定性。我们改变了五个假设:1和2)在第一个质量转移阶段的传质效率和角动量损失,3)具有辐射信封的巨型供体的传质稳定性标准,4)4)演变的恒星发展的有效温度深,对流膜深度对流膜和5)5)Stellar风的质量损失率。我们发现,与传质第一阶段有关的当前不确定性对不同主导渠道的相对重要性产生了巨大影响,而观察到的GW来源的可观察到的人口统计并未显着影响。我们的各种参数对GW来源的种群特性具有复杂的相互关联。因此,鉴于我们当前模型中存在很大的不确定性,仅从GW数据中推断大量二元物理学仍然极具挑战性。
The large number of gravitational wave (GW) detections have revealed the properties of the merging black hole binary population, but how such systems are formed is still heavily debated. Understanding the imprint of stellar physics on the observable GW population will shed light on how we can use the gravitational wave data, along with other observations, to constrain the poorly understood evolution of massive binaries. We perform a parameter study on the classical isolated binary formation channel with the population synthesis code SeBa to investigate how sensitive the properties of the coalescing binary black hole population are on the uncertainties related to first phase of mass transfer and stellar winds. We vary five assumptions: 1 and 2) the mass transfer efficiency and the angular momentum loss during the first mass transfer phase, 3) the mass transfer stability criteria for giant donors with radiative envelopes, 4) the effective temperature at which an evolved star develops a deep convective envelope, and 5) the mass loss rates of stellar winds. We find that current uncertainties related to first phase of mass transfer have a huge impact on the relative importance of different dominant channels, while the observable demographics of GW sources are not significantly affected. Our varied parameters have a complex, interrelated effect on the population properties of GW sources. Therefore, inference of massive binary physics from GW data alone remains extremely challenging, given the large uncertainties in our current models.