标题： 爱因斯坦-标量-高斯-邦尼重力的参数估计与旋减信号 显示英文标题

标题： Parameter estimation for Einstein-dilaton-Gauss-Bonnet gravity with ringdown signals

摘要： 未来基于空间的引力波探测器将在毫赫兹频段以高灵敏度检测引力波，这比地面探测器提供了更多测试引力理论的机会。 准正则模（QNMs）及其在测试引力理论中的应用一直是引力物理领域的重要方面。 在本研究中，我们研究了未来基于空间的引力波探测器，如LISA、太和和天琴，利用双黑洞合并的回音信号来约束爱因斯坦-标量-高斯-博内（EdGB）引力中的无量纲偏离参数的能力。 回音信号由EdGB引力中两个最强的QNMs建模。 考虑到时延干涉测量法，我们计算了不同基于空间的探测器对于回音信号的信噪比（SNR），以分析它们的能力。 采用费舍尔信息矩阵来分析参数估计的准确性，特别关注EdGB引力中的无量纲偏离参数。 还研究了引力波源参数对无量纲偏离参数估计准确性的影响。 我们发现，由于无量纲偏离参数的不确定性随着该参数的减小而增加，因此EdGB引力的约束能力受到限制。 LISA和太和在对大质量黑洞进行更精确的无量纲偏离参数约束方面具有更多优势，而天琴更适合于质量较小的黑洞。 使用贝叶斯推断方法对模拟数据进行参数估计，验证了结论的可靠性。 显示英文摘要

摘要： Future space-based gravitational-wave detectors will detect gravitational waves with high sensitivity in the millihertz frequency band, which provides more opportunities to test theories of gravity than ground-based ones. The study of quasinormal modes (QNMs) and their application to testing gravity theories have been an important aspect in the field of gravitational physics. In this study, we investigate the capability of future space-based gravitational wave detectors such as LISA, TaiJi, and TianQin to constrain the dimensionless deviating parameter for Einstein-dilaton-Gauss-Bonnet (EdGB) gravity with ringdown signals from the merger of binary black holes. The ringdown signal is modeled by the two strongest QNMs in EdGB gravity. Taking into account time-delay interferometry, we calculate the signal-to-noise ratio (SNR) of different space-based detectors for ringdown signals to analyze their capabilities. The Fisher information matrix is employed to analyze the accuracy of parameter estimation, with particular focus on the dimensionless deviating parameter for EdGB gravity. The impact of the parameters of gravitational wave sources on the estimation accuracy of the dimensionless deviating parameter has also been studied. We find that the constraint ability of EdGB gravity is limited because the uncertainty of the dimensionless deviating parameter increases with the decrease of the dimensionless deviating parameter. LISA and TaiJi has more advantages to constrain the dimensionless deviating parameter to a more accurate level for the massive black hole, while TianQin is more suitable for less massive black holes. Bayesian inference method is used to perform parameter estimation on simulated data, which verifies the reliability of the conclusion.