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    Constraining deviations from the Kerr metric via a bumpy parametrization and particle swarm optimization in extreme mass-ratio inspirals

    Xiaobo Zou1, Xingyu Zhong1, Wen-Biao Han2,1,3,4,5,*, and Soumya D. Mohanty6,†

    • 1School of Fundamental Physics and Mathematical Sciences, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China
    • 2Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China
    • 3Taiji Laboratory for Gravitational Wave Universe (Beijing/Hangzhou), University of Chinese Academy of Sciences, Beijing 100049, China
    • 4School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing 100049, China
    • 5State Key Laboratory of Radio Astronomy and Technology, A20 Datun Road, Chaoyang District, Beijing 100101, People’s Republic of China
    • 6Department of Physics and Astronomy, The University of Texas Rio Grande Valley, Brownsville, Texas 78520, USA

    • *Contact author: wbhan@shao.ac.cn
    • †Contact author: soumya.mohanty@utrgv.edu

    Phys. Rev. D 112, 084075 – Published 29 October, 2025

    DOI: https://doi.org/10.1103/ng1f-ml7m

    Abstract

    The measurement of deviations in the Kerr metric using gravitational-wave observations will provide a clear signal of new physics. Previous studies have developed multiple parametrizations (e.g., “bumpy” spacetime) to characterize such deviations in extreme mass-ratio inspirals (EMRIs). These approaches often rely on the Fisher information matrix (FIM) formalism to quantify the constraining power of future space-borne gravitational-wave detectors, such as LISA and Tianqin. For instance, using the analytical kludge waveform model under varying source configurations, such methods have achieved constraint sensitivity levels ranging from 10−4 to 10−2 on the dimensionless bumpy parameter δQ˜ for LISA. In this paper, we advance prior analyses by integrating particle swarm optimization (PSO) with matched filtering under a restricted parameter search range to enforce a high probability of convergence for PSO. Our results reveal a significant number of degenerate peaks in the likelihood function over the signal parameter space with values that exceed the injected one. This extreme level of degeneracy arises from the involvement of the additional bumpy parameter δQ˜ in the parameter space and introduces systematic errors in parameter estimation. We show that these systematic errors can be mitigated using information contained in the ensemble of degenerate peaks, thereby showing a promising potential method for improving local parameter estimation if the other immense challenges of a global search are first solved. This study highlights the critical importance of accounting for such degeneracies, which are absent in FIM-based analyses, and points out future directions for improving EMRI data analysis.

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