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arXiv 2602.22074gr-qc
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超越高斯假设:一种新的稳健统计框架用于引力波数据分析

Beyond Gaussian Assumptions: A new robust statistical framework for gravitational-wave data analysis

University of Minnesota(明尼苏达大学);NSF Institute on Accelerated AI Algorithms for Data-Driven Discovery (A3D3), MIT(美国国家科学基金会加速AI算法驱动发现研究所(A3D3),麻省理工学院);Aristotle University of Thessaloniki(塞萨洛尼基亚里士多德大学);University of California(加州大学);Lawrence Berkeley National Laboratory(劳伦斯伯克利国家实验室)

Argyro Sasli, Minas Karamanis, Nikolaos Karnesis, Michael W. Coughlin, Vuk Mandic, Uroš Seljak, Nikolaos Stergioulas

AI总结:

本文提出一种稳健统计框架,用于处理非高斯噪声的引力波数据分析,通过超几何似然方法在全频域应用,提升鲁棒性和准确性。

摘要:

许多传统算法应用于引力波天文学中依赖高斯噪声假设,但该假设并不总成立。为此,本研究扩展了稳健统计框架,推进了重尾似然方法的先前工作,将双曲似然方法应用于全频域应用。该框架旨在在理想条件下保持高性能,同时提高对非高斯噪声和现实数据中异常值的鲁棒性。我们通过两个关键案例研究展示了该方法的有效性。第一个案例研究分析了模拟激光干涉空间天线(LISA)数据中的大质量黑洞并合事件,其中扩展的双曲似然方法表现与更常用的Whittle似然相当。第二个案例研究考察了使用真实地面引力波数据中的恒星级黑洞并合事件,其中我们的框架表现出更高的鲁棒性,并提供了更准确的参数估计。我们的结果表明,双曲似然方法更好地捕捉了真实的噪声分布,为当前和未来探测器的引力波数据分析提供了一种灵活且物理上合理的替代方法。

英文摘要:

Many traditional algorithms applied in gravitational-wave astronomy rely on the assumption of Gaussian noise, a condition not always met. To meet this need, this study extends a robust statistical framework, advancing previous work on heavy-tailed likelihoods, that adapts the hyperbolic likelihood method for full frequency domain applications. The framework is designed to maintain high performance under ideal conditions while improving robustness against non-Gaussian noise and outliers in real-world data. We demonstrate the efficacy of this approach through two key case studies. The first case study analyzes a massive black hole binary merger in simulated Laser Interferometer Space Antenna (LISA) data with Gaussian noise, showing that the extended hyperbolic likelihood method performs comparably to the more commonly used Whittle likelihood. The second case study examines a stellar-mass black hole binary merger using real ground-based gravitational-wave data containing non-Gaussian noise or overlapping signals, where our framework exhibits increased robustness and yields more accurate parameter estimations. Our results show that the hyperbolic likelihood better captures the true noise distribution, providing a flexible and physically motivated alternative for GW data analysis across current and future detectors.

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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