论文标题

极端质量比灵感的重力波背景

Gravitational wave background from extreme mass ratio inspirals

论文作者

Bonetti, Matteo, Sesana, Alberto

论文摘要

极端质量比灵感(EMRIS),即由紧凑的恒星质量物体组成的二进制系统绕着巨大的黑洞,有望成为即将到来的Lisa任务的主要重力波(GW)源。导致这种系统形成的天体物理过程仍然对这些来源的预测宇宙速率造成了很大的不确定性,至少跨越了三个数量级。由于丽莎可以单独解决最高$ z \ gtrsim1 $的EMRIS,因此在其检测阈值下方的信号集合将添加不连贯地形成未解决的混淆噪声,可以正式描述为随机背景。我们通过考虑一系列以天体物理动机的EMRI形成场景来对这种背景进行广泛的研究,涵盖了当前的不确定性。我们发现,对于大多数天体物理模型,该信号很容易被丽莎检测到,信号与噪声比为数百。在信托EMRI模型中 - 预测任务操作过程中数百次EMRI检测 - 背景水平与Lisa噪声相当,影响仪器在3 MHz左右的性能。在极端情况下,这种背景甚至可以在2-10 MHz频率范围内“消除”整个Lisa灵敏度桶。这表明需要更好地了解Emris的天体物理学,以全面评估LISA任务潜力。

Extreme mass ratio inspirals (EMRIs), i.e. binary systems comprised by a compact stellar-mass object orbiting a massive black hole, are expected to be among the primary gravitational wave (GW) sources for the forthcoming LISA mission. The astrophysical processes leading to the formation of such systems still remain poorly understood, resulting into large uncertainties in the predicted cosmic rate of these sources, spanning at least three orders of magnitude. As LISA can individually resolve mostly EMRIs up to $z\gtrsim1$, the ensemble of signals below its detection threshold will add up incoherently forming an unresolved confusion noise, which can be formally described as a stochastic background. We perform an extensive study of this background by considering a collection of astrophysically motivated EMRI formation scenarios, spanning current uncertainties. We find that, for most astrophysical models, this signal is easily detectable by LISA, with signal to noise ratios of several hundreds. In fiducial EMRI models -- predicting hundreds of EMRI detections during mission operations -- the background level is comparable to the LISA noise, affecting the performance of the instrument around 3 mHz. In extreme cases, this background can even "erase" the whole LISA sensitivity bucket in the 2-10 mHz frequency range. This points to the need of a better understanding of EMRIs' astrophysics for a full assessment of the LISA mission potential.

扫码加入交流群

加入微信交流群

微信交流群二维码

扫码加入学术交流群,获取更多资源