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Globular clusters and supermassive black holes in galaxies: further analysis and a larger sample

机译:球状星团和星系中的超大质量黑洞:进一步   分析和更大的样本

摘要

We explore several correlations between various large-scale galaxyproperties, particularly total globular cluster population (N_GCS), the centralblack hole mass (M_BH), velocity dispersion (nominally sigma_e), and bulge mass(M_dyn). Our data sample of 49 galaxies, for which both N_GC and M_BH areknown, is larger than used in previous discussions of these two parameters andwe employ the same sample to explore all pairs of correlations. Further, withinthis galaxy sample we investigate the scatter in each quantity, with emphasison the range of published values for sigma_e and effective radius (R_e). Wefind that these two quantities in particular are difficult to measureconsistently and caution that precise intercomparison of galaxy propertiesinvolving R_e and sigma_e is particularly difficult. Using both chi^2 and Monte Carlo Markov Chain (MCMC) fitting techniques, weshow that quoted observational uncertainties for all parameters are too smallto represent the true scatter in the data. We find that the correlation betweenM_dyn and N_GC is stronger than either the M_BH-sigma_e or M_BH-N_GC relations.We suggest that this is because both the galaxy bulge population ans N_GC werefundamentally established at an early epoch during the same series ofstar-forming events. By contrast, although the seed for M_BH was likely formedat a similar epoch, its growth over time is less similar from galaxy to galaxyand thus less predictable.
机译:我们探索了各种大型星系属性之间的几种相关性,特别是总球状星团总数(N_GCS),中心黑洞质量(M_BH),速度色散(名义上为sigma_e)和凸起质量(M_dyn)。我们已知的N_GC和M_BH的49个星系的数据样本大于之前讨论的这两个参数所使用的样本,并且我们使用相同的样本来探索所有对的相关性。此外,在这个星系样本中,我们研究了每个量的散射,重点是sigma_e和有效半径(R_e)的已发布值的范围。我们发现,这两个量尤其难以一致地进行测量,并警告说,涉及R_e和sigma_e的星系性质的精确比对特别困难。使用chi ^ 2和蒙特卡洛马尔可夫链(MCMC)拟合技术,我们显示所有参数的引用观测不确定性太小,无法代表数据中的真实散布。我们发现M_dyn与N_GC之间的相关性强于M_BH-sigma_e或M_BH-N_GC关系,这表明这是因为在同一系列恒星形成事件中,两个星系凸起种群和N_GC都是在较早的时期基本建立的。相比之下,尽管M_BH的种子可能是在相似的时期形成的,但随着时间的推移,从银河系到银河系的增长不太相似,因此难以预测。

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