使用连续归一化流对大质量黑洞双星并合进行快速参数估计
Rapid Parameter Estimation for Merging Massive Black Hole Binaries Using Continuous Normalizing Flows
AI总结：
针对大质量黑洞双星并合参数估计计算成本高的问题，提出基于连续归一化流并结合参数变换的方法，首次实现11维快速推断，实验后验分布与嵌套抽样相当。
AI中文摘要：
探测大质量黑洞双星（MBHBs）的并合是LISA、太极和天琴等天基引力波天文台的主要目标之一。对并合MBHBs进行快速准确的参数估计，对于所有可分辨源的全局拟合以及引力波信号的天体物理解释具有重要意义。然而，此类分析通常需要巨大的计算成本。为应对这些挑战，受生成模型最新进展的启发，我们探索了连续归一化流（CNFs）在MBHBs参数估计中的应用。具体而言，我们采用线性插值和三角插值方法构建CNFs训练的传输路径。此外，我们创新性地引入了一种基于探测器响应函数对称性的参数变换方法。该变换集成在CNFs中，使我们能够使用简化数据集训练模型，然后对更一般的数据进行参数估计，因此也是提高训练速度的关键因素。总之，我们首次在全面合理的参数范围内，利用CNFs在存在天体物理混淆噪声的情况下实现了MBHBs完整且无偏的11维快速推断。在基于模拟数据的实验中，我们的模型产生的后验分布与嵌套抽样获得的后验分布相当。
英文摘要：
Detecting the coalescences of massive black hole binaries (MBHBs) is one of the primary targets for space-based gravitational wave observatories such as LISA, Taiji, and Tianqin. The fast and accurate parameter estimation of merging MBHBs is of great significance for the global fitting of all resolvable sources, as well as the astrophysical interpretation of gravitational wave signals. However, such analyses usually entail significant computational costs. To address these challenges, inspired by the latest progress in generative models, we explore the application of continuous normalizing flows (CNFs) on the parameter estimation of MBHBs. Specifically, we employ linear interpolation and trig interpolation methods to construct transport paths for training CNFs. Additionally, we creatively introduce a parameter transformation method based on the symmetry in the detector's response function. This transformation is integrated within CNFs, allowing us to train the model using a simplified dataset, and then perform parameter estimation on more general data, hence also acting as a crucial factor in improving the training speed. In conclusion, for the first time, within a comprehensive and reasonable parameter range, we have achieved a complete and unbiased 11-dimensional rapid inference for MBHBs in the presence of astrophysical confusion noise using CNFs. In the experiments based on simulated data, our model produces posterior distributions comparable to those obtained by nested sampling.
