玻尔兹曼遇到洛伦兹：黑洞回波的替代模型
Boltzmann Meets Lorentz: A Surrogate Model for Black Hole Echoes
摘要：
黑洞视界的存在还没有得到严格的观测证明，事实上也不可能。然而，通过观测探测可能的近地平线结构的引力波回波，可以建立替代方案。这些回声被认为是在奇异的致密天体中产生的，这些天体是没有水平线的，并且在它们的光环内有一个部分反射的“墙”，在其中产生一个引力扰动可能产生回声的空腔，同时在每次通过时通过角动量屏障泄漏出来。回波的特征信号是由几乎均匀间隔的频谱共振组成的梳状结构。虽然近似正确，但偏离这个简单的图像可能会导致严重的观测信号损失。在本文中，我们将探索这种微妙的最新结果的回声来源和几何。一个物理激励的回波模型，然后开发为洛伦兹谱线的总和，参数化的地平线帧频率和腔的大小的函数。我们最终得到的光谱只与黑洞的质量和自旋以及近视界物理学的紫外尺度有关。然后，我们将此模型应用于搜索LIGO/Virgo中具有最大振铃信号的引力波事件（即GW 190521）中的回波。我们将我们的研究结果解释为合并后回波中的分数能量的测量等于$E_{echoes} / E_{GR} = 8.9 \pm 4.5\%$，其中不确定性范围代表90%的可信区域。这个结果的鲁棒性进行了测试，对噪声背景和模拟注射，我们发现，信号持续通过修改模型和数据搜索的变化。
英文摘要：
The existence of black hole horizons has not been strictly proven observationally, and indeed it may not be possible to do so. However, alternatives may be established by the observation of gravitational wave echoes that probe possible near-horizon structure. These echoes are proposed to be generated in exotic compact objects that are horizonless and feature a partially reflecting "wall" inside their light rings, creating a cavity in which gravitational perturbations may echo, while leaking out through the angular momentum barrier with each pass. The characteristic signature of echoes is a comb of nearly evenly spaced spectral resonances. While approximately true, deviations from this simple picture can lead to severe observational signal losses. In this paper, we explore such subtleties with the latest results for echo sourcing and geometry. A physically motivated echo model is then developed as a sum over Lorentzian spectral lines, parametrized by functions of the horizon frame frequency and the size of the cavity. Our final spectrum is a function of only the mass and spin of the black hole, as well as the UV scale of the near-horizon physics. We then apply this model in a search for echoes in the gravitational wave event with the loudest ringdown signal in LIGO/Virgo, i.e. GW190521. We interpret our findings as a measurement of the fractional energy in post-merger echoes equal to $E_{echoes} / E_{GR} = 8.9 \pm 4.5\%$, where the uncertainty range represents the 90% credible region. The robustness of this result is tested against noise backgrounds and simulated injections, and we find that a signal persists through modifications to the model and changes in the data search.
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