Rapid Detection and Inference of Extreme-Mass-Ratio Inspirals in LISA: Divide and Conquer

About

Extreme-mass-ratio inspirals (EMRIs) are key gravitational-wave sources for the Laser Interferometer Space Antenna (LISA), but their detection and parameter inference are computationally challenging due to the extreme concentration of posterior distributions within vast prior volumes. In this work, we introduce a novel divide-and-conquer strategy that reformulates global inference as a hierarchical identification problem. Our approach iteratively localizes the posterior mode through a coarse-to-fine procedure based on ordinal classification, progressively restricting the parameter space while preserving the true signal region. A transformer-based neural network is trained at each stage to identify the most probable parameter subregions, enabling exponential reduction of the search volume with only a few refinement steps. Once the parameter space is reduced to the Fisher-information scale, standard local sampling methods efficiently recover the full joint posterior. We demonstrate that this method achieves rapid and accurate intrinsic parameter estimation for EMRIs in simulated LISA data, dramatically reducing computational costs compared to traditional global sampling techniques. This framework provides a scalable and efficient pathway for real-time EMRI detection and inference in the LISA era.

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