基于自编码器的环衰减准正规模参数估计
Parameter Estimation of Ringdown Quasinormal Modes with Autoencoder
- Tokyo City University（东京都市大学）
- Tokyo Metropolitan University（东京都立大学）
- The University of Tokyo（东京大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出基于自编码器的框架，用于多分量环衰减准正规模的参数估计，在受控波形上实现良好重建与参数恢复，验证了物理信息推断的可行性。
AI中文摘要：
双黑洞并合产生的环衰减引力波可以建模为准正规模（QNMs）的叠加，其频率和激发因子编码了残余Kerr黑洞的性质。由于模式重叠和噪声的影响，可靠地提取多个QNM分量具有挑战性。我们开发了一个基于自编码器的框架用于多分量QNM分析，其中潜空间被训练以表示单个模式的物理参数，从而在统一框架内实现波形去噪和参数估计。使用由Kerr QNMs的有限和构成的受控模型波形，这些波形采用最近建立的高精度频率和激发因子，包括其在共振激发附近非平凡的自旋依赖性，我们在划分的自旋区间上评估了该方法。该模型在域内实现了对八分量输入波形中两个最长寿命分量的良好波形重建和参数恢复，而当验证自旋远超出训练范围时，其性能会下降。在一个选定的自旋区间内，该框架还以良好的整体一致性恢复了八分量波形的32个参数。这些结果表明，基于物理信息的自编码器推断对于指定的多分量环衰减波形族是可行的，并促使使用逐渐更真实的信号进行进一步测试。
英文摘要：
Ringdown gravitational waves from binary black hole mergers can be modeled as superpositions of quasinormal modes (QNMs), whose frequencies and excitation factors encode properties of the remnant Kerr black hole. Reliable extraction of multiple QNM components is challenging because of mode overlap and noise. We develop an autoencoder-based framework for multi-component QNM analysis, in which the latent space is trained to represent the physical parameters of individual modes, enabling waveform denoising and parameter estimation within a common framework. Using controlled model waveforms constructed as finite sums of Kerr QNMs with recently established high-precision frequencies and excitation factors, including their nontrivial spin dependence near resonant excitation, we assess the method across partitioned spin intervals. The model achieves good in-domain waveform reconstruction and parameter recovery for the two longest-lived components of eight-component input waveforms, while its performance degrades when the validation spins lie far outside the training range. In a selected spin interval, the framework also recovers the 32 parameters of an eight-component waveform with good overall agreement. These results demonstrate the feasibility of physics-informed autoencoder-based inference for a prescribed multi-component ringdown waveform family and motivate further tests with progressively more realistic signals.
