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arXiv 2511.07186gr-qcastro-ph.IM
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通过深度学习利用透镜效应识别和表征畸变的引力波

Identification and characterization of distorted gravitational waves by lensing using deep learning

Niels Bohr Institute（尼尔斯·玻尔研究所）

Juno C. L. Chan, Lorena Magaña Zertuche, Jose María Ezquiaga, Rico K. L. Lo, Luka Vujeva, Joey Bowman

备注：
17 pages, 12 figures, matches PRD published version
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AI总结：

DINGO-lensing通过深度学习高效识别和表征引力波透镜效应，显著提升参数估计效率和精度。

摘要：

引力波在传播过程中可能会受到中间质量分布的扭曲，导致频率依赖性的调制，从而在观测到的波形上留下独特的印记。传统的采样方法进行引力波透镜效应的贝叶斯推断成本高昂，且随着引力波目录的迅速增长，问题变得更加严重。此外，评估被透镜效应影响的候选信号的统计显著性需要成千上万，甚至数百万次的模拟来估计背景噪声波动和波形系统误差，这在使用标准采样器时是不可行的。我们提出了一种新的方法，DINGO-lensing，用于对被透镜效应影响的引力波进行推断，扩展了神经后验估计框架DINGO。通过将我们的结果与传统采样器的结果进行比较，我们表明，对被透镜效应影响的引力波参数估计的计算时间可以从几周减少到几秒，同时在后验分布和证据比方面保持准确性。我们使用LIGO探测器设计灵敏度的噪声和一个能够容纳两个重叠图像且具有恒定π/2相位偏移的透镜模型来训练我们的神经网络。我们展示了时间延迟的透镜参数可以以毫秒精度恢复。我们还证明了我们的网络可以识别被点质量衍射的信号，突显了其在搜索中的灵活性。通过模拟数千个被透镜效应影响和未被透镜效应影响的事件，我们确定了不同源特性如何影响可检测性。DINGO-lensing为未来观测运行中识别和表征引力波透镜效应事件提供了一种可扩展且高效的方法。

英文摘要：

Gravitational waves (GWs) can be distorted by intervening mass distributions while propagating, leading to frequency-dependent modulations that imprint a distinct signature on the observed waveforms. Bayesian inference for GW lensing with conventional sampling methods is costly, and the problem is exacerbated by the rapidly growing GW catalog. Moreover, assessing the statistical significance of lensed candidates requires thousands, if not millions, of simulations to estimate the background from noise fluctuations and waveform systematics, which is infeasible with standard samplers. We present a novel method, DINGO-lensing, for performing inference on lensed GWs, extending the neural posterior estimation framework DINGO. By comparing our results with those using conventional samplers, we show that the compute time of parameter estimation of lensed GWs can be reduced from weeks to seconds, while preserving accuracy both in the posterior distributions and the evidence ratios. We train our neural networks with LIGO detector noise at design sensitivity and a lens model that accommodates two overlapping images with a constant $π/2$ phase shift. We show that the lensing parameters are recovered with millisecond precision for the time delays. We also demonstrate that our network can identify signals diffracted by point masses, highlighting its flexibility for searches. By simulating thousands of lensed and nonlensed events, we determine how the detectability changes with different source properties. DINGO-lensing provides a scalable and efficient avenue for identifying and characterizing gravitationally lensed GW events in the upcoming observing runs.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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