论文标题

带电的高斯 - 骨网黑色孔的自发标量:分析处理

Spontaneous Scalarization of Charged Gauss-Bonnet Black Holes: Analytic Treatment

论文作者

Jiang, Shun

论文摘要

最近,通过考虑标量场和高斯 - 骨网之间的非平凡耦合,Schwarzschild黑洞可能允许常规的标量毛茸茸的配置。对这种自发标量现象的数值研究表明,耦合参数$ \barη$是否属于离散集$ \barη\ in \ {\barη^-_ n,\barη^-n^+_ n^+_ n \}^+_ n \ \}^{n = \ infty} _ {有趣的是,HOD找到对应于黑洞线性化标量字段配置的耦合参数$ \barη^+_ n $具有渐近普遍行为$Δ_N\ equiv \ equiv \ equrt {\barη_{n+1}}}}}} - \ sqrt {\ sqrt {他为数值观察到的普遍行为提供了非常紧凑的分析解释。在本文中,我们研究了这种有趣的现象,我们研究了RN黑洞中的耦合参数行为,标量场和高斯 - 骨网不变之间的非平凡耦合。与Schwarzschild案例不同,耦合参数只能采用正值,在这种情况下,耦合参数可以呈正值或负值值。因此,研究耦合参数在这种情况下是否具有相似的渐近行为是很有趣的。通过检查数值数据,我们发现正面和负参数都存在类似的渐近行为。我们还将分析结果与数值数据进行了比较。我们发现分析结果与数值数据非常吻合。

Recently, by considering nontrivial couplings between scalar fields and the Gauss-Bonnet invariant, the Schwarzschild black hole may allow regular scalar hairy configurations. The numerical studies of this spontaneous scalarization phenomenon show if the coupling parameter $\barη$ belongs to discrete sets $\barη\in\{\barη^-_n,\barη^+_n\}^{n=\infty}_{n=0}$, the black hole can support regular scalar hairy configurations. Interestingly, Hod finds the coupling parameter $\barη^+_n$ which correspond to the black hole linearized scalar field configurations has an asymptotic universal behavior $Δ_n\equiv\sqrt{\barη_{n+1}}-\sqrt{\barη_{n}} \simeq2.72$. He provides a remarkably compact analytical explanation for the numerically observed universal behavior. Motivated by this interesting phenomenon, in this paper, we study the coupling parameter behavior in RN black hole with a nontrivial coupling between scalar fields and the Gauss-Bonnet invariant. Different from Schwarzschild case where the coupling parameter can only take positive values, in this case, the coupling parameter can take positive or negativie values. Therefore, it is interesting to investigate whether the coupling parameter has a similar asymptotic behavior in this situation. By examining numerical data, we find there is a similar asymptotic behavior for both positive and negative parameters. We also compare the analytical results with the numerical data. We find analytical results agree well with the the numerical data.

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