论文标题
黑洞质量功能及其演变 - 爱因斯坦望远镜的第一个预测
Black hole mass function and its evolution -- the first prediction for the Einstein Telescope
论文作者
论文摘要
关于黑洞质量功能(BHMF)及其演变的知识将有助于了解BHS的起源以及BH二进制文件在宇宙历史的不同阶段的形成方式。我们证明了未来第三代重力波(GW)检测器的能力 - 爱因斯坦望远镜(ET)推断BHMF的斜率及其使用红移的演变。我们对来自二进制BH系统(BBH)的CHIRP信号的测量值进行了蒙特卡洛模拟,ET可以检测到,包括BH质量及其光度距离($ d_l $)。我们使用每个二元系统中的主要黑洞质量来推断BHMF作为倾斜参数为$α$的幂律函数。考虑到可以通过测量值的不确定性和选择效果引起的偏见,我们进行了数值测试,发现只有ET注册的一千个GW事件(其年度检测率的$ \ sim1 \%$ $)可以准确地推断出$α$的精确$α$。此外,我们研究了我们方法恢复一个方案的方法的有效性,其中$α$以红移为$α(z)=α_0 +α_1 + frac {z} {1 + z} $。采用一千个GW事件并使用$ d_l $作为红移估算器,我们的测试表明,可以准确地推断出不确定性级别的$ \ sim0.5 $的不确定性水平,可以推断出不断发展的参数$α_1$。我们的数值测试验证了我们方法的可靠性。可以直接对我们假设的几个参数集来信任推断参数的不确定性级别,但不应将其视为一般情况的通用级别。
The knowledge about the black hole mass function (BHMF) and its evolution would help to understand the origin of the BHs and how BH binaries formed at different stages of the history of the Universe. We demonstrate the ability of future third generation gravitational wave (GW) detector -- the Einstein Telescope (ET) to infer the slope of the BHMF and its evolution with redshift. We perform the Monte Carlo simulation of the measurements of chirp signals from binary BH systems (BBH) that could be detected by ET, including the BH masses and their luminosity distances ($d_L$). We use the mass of a primary black hole in each binary system to infer the BHMF as a power-law function with slope parameter as $α$. Taking into account the bias that could be introduced by the uncertainty of measurements and by the selection effect, we carried out the numerical tests and find that only one thousand of GW events registered by ET ($\sim1\%$ amount of its yearly detection rate) could accurately infer the $α$ with a precision of $α\sim0.1$. Furthermore, we investigate the validity of our method to recover a scenario where $α$ evolves with redshift as $α(z) = α_0 + α_1\frac{z}{1+z}$. Taking a thousand of GW events and using $d_L$ as the redshift estimator, our tests show that one could infer the value of evolving parameter $α_1$ accurately with the uncertainty level of $\sim0.5$. Our numerical tests verify the reliability of our method. The uncertainty levels of the inferred parameters can be trusted directly for the several sets of the parameter we assumed, yet shouldn't be treated as a universal level for the general case.