English

Constructing a gravitational wave analysis pipeline for extremely large mass ratio inspirals

High Energy Astrophysical Phenomena 2026-02-02 v1 Instrumentation and Methods for Astrophysics General Relativity and Quantum Cosmology

Abstract

Extremely large mass-ratio inspirals (XMRIs), consisting of a brown dwarf orbiting a supermassive black hole, emit long-lived and nearly monochromatic gravitational waves in the millihertz band and constitute a promising probe of strong-field gravity and black-hole properties. However, dedicated data-analysis pipelines for XMRI signals have not yet been established. In this work, we develop, for the first time, a hierarchical semi-coherent search pipeline for XMRIs tailored to space-based gravitational-wave detectors, with a particular focus on the TianQin mission. The pipeline combines a semi-coherent multi-harmonic F\mathcal{F}-statistic with particle swarm optimization, and incorporates a novel eccentricity estimation method based on the relative power distribution among harmonics. We validate the performance of the pipeline using simulated TianQin data for a Galactic center XMRI composed of a brown dwarf and Sgr A*. For a three-month observation, the pipeline successfully recovers the signal and achieves high-precision parameter estimation, including fractional uncertainties of <106<10^{-6} in the orbital frequency, 103\lesssim10^{-3} in the eccentricity, 2×103\lesssim2\times10^{-3} in the black-hole mass, and 103\lesssim10^{-3} in the black-hole spin. Our framework establishes a practical foundation for future XMRI searches with space-based detectors and highlights the potential of XMRIs as precision probes of stellar dynamics and strong-field gravity in the vicinity of supermassive black holes.

Keywords

Cite

@article{arxiv.2601.22464,
  title  = {Constructing a gravitational wave analysis pipeline for extremely large mass ratio inspirals},
  author = {Tian-Xiao Wang and Yan Wang and Alejandro Torres-Orjuela and Yi-Ren Lin and Hui-Min Fan and Verónica Vázquez-Aceves and Yi-Ming Hu},
  journal= {arXiv preprint arXiv:2601.22464},
  year   = {2026}
}

Comments

20 pages, 9 figures, submitted to PRD, comments welcome