arXiv 2610.196822026-10-15gr-qc

基于深度学习的极端质量比旋进系统内禀参数的分层参数估计

Hierarchical Parameter Estimation for Intrinsic Parameters of Extreme-mass-ratio Inspirals Using Deep Learning

Huai’an University（淮阴大学）；Shanghai Astronomical Observatory, Chinese Academy of Sciences（中国科学院上海天文台）；Hangzhou Institute for Advanced Study, UCAS（中国科学院大学杭州高等研究院）；Key Laboratory of Radio Astronomy and Technology, Chinese Academy of Sciences（中国科学院射电天文技术重点实验室）；Shanghai Frontiers Science Center for Gravitational Wave Detection（引力波探测上海前沿科学中心）；Center for Gravitational Wave Experiment, National Microgravity Laboratory, Institute of Mechanics, Chinese Academy of Sciences（中国科学院力学研究所国家微重力实验室引力波实验中心）

Ping Shen, Wen-Biao Han, Minghui Du

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AI总结：

针对极端质量比旋进系统内禀参数估计的高成本问题，提出配备双注意力机制的深度学习框架，大幅缩小参数空间体积，可快速预筛选参数空间以节省计算成本，适用于太极等空间引力波探测器。

摘要：

极端质量比旋进系统（EMRI）是未来空间引力波（GW）探测器的核心探测目标，但其精确参数估计受限于波形复杂性与高维参数空间。本研究提出一种深度学习框架，无需依赖外禀参数的详细先验即可恢复EMRI的7个内禀参数。该方法首先采用U-Net从含噪时频数据中提取鲁棒信号特征，随后将特征输入配备双注意力机制的多通道卷积神经网络（CNN），该网络解耦时间与频率维度，联合建模长程时间演化与持续谱结构，从而提升对含噪数据中EMRI时频脊的敏感性。值得注意的是，该方法将参数{log M, a, μ, e₀, p₀}的搜索范围缩小约10²倍，将参数{t_p, x_I₀}的搜索范围缩小约10¹倍，有效将7维参数空间的总体积缩小约10⁸倍。这种大幅缩减可实现内禀参数空间的快速预筛选，大幅节省后续全参数估计的计算成本。尽管本框架是针对太极（Taiji）任务规格开发的，但该方法也适用于其他空间引力波探测器的数据。

英文摘要：

Extreme-mass-ratio inspirals (EMRIs) stand as prime targets for future space-borne gravitational wave (GW) detectors, yet their precise parameter estimation is severely hindered by waveform complexity and high-dimensional parameter spaces. In this work, we propose a deep learning framework capable of recovering the seven intrinsic EMRI parameters without relying on detailed priors for extrinsic parameters. Our approach first employs a U-Net to extract robust signal features from noisy time-frequency data. These features are then fed into a multi-channel convolutional neural network (CNN) equipped with a dual attention mechanism, which decouples the time and frequency dimensions to jointly model long-range temporal evolution and persistent spectral structures, thereby enhancing sensitivity to EMRI time-frequency ridges in noisy data. Remarkably, this method reduces the search ranges for the parameters $\{\log M, a, μ, e_0, p_0 \}$ and $\{t_p, {x_I}_0 \}$ by factors of $\sim 10^{2}$ and $\sim 10^{1}$, respectively, effectively shrinking the total volume of the seven-dimensional parameter space by $\sim 10^{8}$. Such a dramatic reduction enables a rapid pre-screening of the intrinsic parameter space, substantially saving computational cost for subsequent full parameter estimation. Although our framework is developed with Taiji mission specifications in mind, the methodology is applicable to data from other space-borne GW detectors.

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