Comparing simple quasar demographics models
Comparing simple quasar demographics models
复制标题
比较简单的类星体人口统计模型
DOI：
10.1093/mnras/stu1821
复制
发表时间：
2014
影响因子：
4.8
通讯作者：
C. Conroy
中科院分区：
文献类型：
--
作者：
M. Veale;M. White;C. Conroy
作者：Veale，M;白色，M; Conroy，C|摘要：本文探讨了几个简单的模型变化之间的联系类星体，星系，暗物质晕的红移为1 l z l 6。这些模型的一个关键组成部分是，我们执行一个自洽的黑洞（BH）的历史跟踪BH质量和BH的增长率在所有的红移。我们用一个简单的常数密度程序连接红移的物体，并选择一个基准模型，BH和星系增长率之间的关系是线性的，并以一种简单的方式与红移的演变。在这个基准模型中，我们发现类星体的光度函数（QLF）计算的“内在”的光度的基础上的BH质量或BH的增长率，然后选择一个模型的类星体的变化与对数正态分布或截断幂律分布的瞬时光度。这给出了四个模型的变化，我们拟合到观测到的QLF在每个红移。有了最佳拟合模型，我们对四种基准模型进行了详细的比较，并探索了BH-星系关系的基准模型的变化。每个模型的变化可以成功地适合所观察到的QLF，其形状通常是由“内在”的光度在暗端和散射由于在明亮的end.We的变化专注于占物理上不同的模型可以做出这样类似的预测的原因，并确定什么样的观测数据或物理参数是最重要的，在打破模型之间的简并。
Author(s): Veale, M; White, M; Conroy, C | Abstract: This paper explores several simple model variations for the connections among quasars, galaxies, and dark matter haloes for redshifts 1 l z l 6. A key component of these models is that we enforce a self-consistent black hole (BH) history by tracking both BH mass and BH growth rate at all redshifts. We connect objects across redshift with a simple constantnumber- density procedure, and choose a fiducial model with a relationship between BH and galaxy growth rates that is linear and evolves in a simple way with redshift. Within this fiducial model, we find the quasar luminosity function (QLF) by calculating an 'intrinsic' luminosity based on either the BH mass or BH growth rate, and then choosing a model of quasar variability with either a lognormal or truncated power-law distribution of instantaneous luminosities. This gives four model variations, which we fit to the observed QLF at each redshift. With the best-fitting models in hand, we undertake a detailed comparison of the four fiducial models, and explore changes to our fiducial model of the BH-galaxy relationship. Each model variation can successfully fit the observed QLF, the shape of which is generally set by the 'intrinsic' luminosity at the faint end and by the scatter due to variability at the bright end.We focus on accounting for the reasons why physically different models can make such similar predictions, and on identifying what observational data or physical arguments are most essential in breaking the degeneracies among models.