条件变分自编码器的黑洞光谱学
Black Hole Spectroscopy with Conditional Variational Autoencoder
AI总结：
本文提出基于条件变分自编码器的机器学习框架，用于加速黑洞振铃参数估计，在克尔假设下推断残余性质，并扩展到膜世界引力偏差，与贝叶斯分析对比验证性能。
摘要：
引力波为在强场区域检验广义相对论提供了独特机会，使得从观测数据中提取关键物理参数成为可能。传统的基于似然推断的方法虽然稳健，但在高维参数空间中（例如当包含多个振铃模式或超越克尔偏差时）计算成本高昂。在本文中，我们探索了基于条件变分自编码器的机器学习框架的实现，用于加速振铃参数估计。作为首次应用，我们使用神经网络在克尔假设下推断最终黑洞的残余性质。我们使用与先进LIGO灵敏度一致的模拟振铃波形展示了该算法的性能，并与贝叶斯分析结果进行了比较。我们进一步将该框架扩展到克尔范式之外，通过纳入膜世界引力中预测的偏差。
英文摘要：
Gravitational waves provide a unique opportunity to test general relativity in the strong-field regime, enabling the extraction of key physical parameters from observational data. Traditional likelihood-based inference methods, while robust, become computationally expensive in high-dimensional parameter spaces, such as when incorporating multiple ringdown modes or beyond Kerr deviations. In this paper, we explore the implementation of a conditional variational autoencoder-based machine-learning framework for accelerated ringdown parameter estimation. As a first application, we use the neural network to infer the remnant properties of a final black hole under the Kerr hypothesis. We demonstrate the performance of this algorithm with simulated ringdown waveforms consistent with advanced LIGO sensitivity and compare with Bayesian analysis results. We further extend the framework beyond the Kerr paradigm by incorporating deviations predicted in braneworld gravity.
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