AI 中文总结
该研究针对通用极端质量比旋近源的相干端到端搜寻难题，利用次级极大值的结构设计降维轮廓似然与分层策略，实现了宽先验下的EMRI参数估计，在LISA噪声中成功恢复高信噪比信号。
AI 中文摘要
极端质量比旋近源（EMRI）编码了超过$10^5$个强场轨道周期，是空间引力波干涉仪的关键观测目标，但在天体物理宽先验范围内对通用系统的相干恢复仍未得到解决。连续的模拟LISA数据挑战（MLDCs）、LISA数据挑战（LDCs）和太极数据挑战（TDCs）尚未给出此类先验下盲EMRI探测与参数恢复的完整通用可靠解决方案。核心障碍是“干草堆里找针”式的似然问题：6个相位演化参数分布在广阔域中，在众多次级极大值间产生了极窄的全局极大值。我们证明，更高似然的次级极大值会逐渐集中在全局极大值周围，因此可用于引导搜寻体积的自适应收缩。我们利用这一结构，通过降维轮廓似然和相干分层策略，在完整14维参数空间中搜寻EMRI信号，实现了天体物理宽先验下通用EMRI的首次端到端相干参数估计。在平稳高斯LISA噪声中，该搜寻恢复了两个半年期分析近似信号，其信噪比接近50，拟合优度分别为0.989和0.971，相位演化参数的分数误差为$10^{-3}$至$10^{-2}$，距离的分数误差接近3%，天球位置的误差小于0.1弧度。该方法将次级极大值转化为相干分层搜寻的引导。
英文摘要
Extreme-mass-ratio inspirals (EMRIs) encode more than $10^5$ strong-field orbital cycles and are key targets for space-borne gravitational-wave interferometers, yet coherent recovery of generic systems over astrophysically broad priors remains unresolved. Successive Mock LISA, LISA, and Taiji Data Challenges (MLDCs, LDCs, and TDCs) have not yet produced a complete, generally reliable solution for blind EMRI detection and parameter recovery across such priors. The central obstacle is a needle-in-a-haystack likelihood: six phase-evolution parameters span a vast domain, producing an exceptionally narrow global maximum amid numerous secondary maxima. We show that higher-likelihood secondary maxima concentrate progressively around the global maximum and can therefore guide an adaptive contraction of the search volume. We exploit this structure through a reduced-dimensional profile likelihood and a coherent hierarchical strategy to search for EMRI signals across the full 14-dimensional parameter space. This enables the first end-to-end coherent parameter estimation for generic EMRIs with astrophysically broad priors. In stationary Gaussian LISA noise, the search recovers two half-year analytical-kludge signals with signal-to-noise ratios near 50, yielding fitting factors of 0.989 and 0.971, fractional errors of $10^{-3}$--$10^{-2}$ in the phase-evolution parameters and near $3\%$ in the distance, and error of less than $0.1$ radian in the sky location. The method turns secondary maxima into guides for a coherent hierarchical search.
Comments6 pages, 2 figures
