标题： 序贯模拟推理在极端质量比合并中的应用 显示英文标题

标题： Sequential simulation-based inference for extreme mass ratio inspirals

摘要： 极端质量比共舞对即将发射的空间引力波探测器如LISA来说，在搜索和参数估计方面构成了一个巨大的挑战。它们的信号长且形态复杂，这意味着它们携带了大量关于其源的信息，但也很难被搜索和分析。 我们探讨了如何使用顺序模拟推理方法，特别是截断边缘神经比率估计，来解决围绕极端质量比共舞数据分析的一些挑战。我们展示了这种方法可以有效地将复杂的11维搜索参数空间缩小$10^6-10^7$倍，并为非自旋极端质量比共舞提供一维边缘提议分布。 我们讨论了这种方法当前的局限性，并将其置于未来空间引力波数据分析全球策略的大背景下。 显示英文摘要

摘要： Extreme mass-ratio inspirals pose a difficult challenge in terms of both search and parameter estimation for upcoming space-based gravitational-wave detectors such as LISA. Their signals are long and of complex morphology, meaning they carry a large amount of information about their source, but are also difficult to search for and analyse. We explore how sequential simulation-based inference methods, specifically truncated marginal neural ratio estimation, could offer solutions to some of the challenges surrounding extreme-mass-ratio inspiral data analysis. We show that this method can efficiently narrow down the volume of the complex 11-dimensional search parameter space by a factor of $10^6-10^7$ and provide 1-dimensional marginal proposal distributions for non-spinning extreme-mass-ratio inspirals. We discuss the current limitations of this approach and place it in the broader context of a global strategy for future space-based gravitational-wave data analysis.