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Loosely-coherent inference of long-inspiral gravitational-wave sources for robustness against modeling and data outliers

Shubham Kejriwal*

  • *Contact author: shubhamkejriwal@u.nus.edu
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Phys. Rev. D 114, 104028 – Published 6 November, 2026

DOI: https://doi.org/10.1103/w643-mr38

Abstract

The Laser Interferometer Space Antenna (LISA) will detect gravitational waves (GWs) from dozens of extreme- and intermediate-mass-ratio inspirals. These sources will stay in band for months to years, offering extraordinary scientific potential. However, their fully phase-coherent analysis standard in current pipelines imposes stringent waveform accuracy requirements; failing to model the signal over such long durations can result in significant systematic biases. To address this, we formulate a robust Bayesian inference framework that segments the data into blocks, infers each block coherently, and recombines the results using loosely-coherent phase information. By restricting full phase tracking to much shorter block durations, this approach prevents significant accumulation of phase errors. We implement this methodology in (Segmented Posteriors for Long-Inspiral Templates (split), a graphics-processing-unit-accelerated Python package. Applying split to an environment-rich injection, we demonstrate that, while a fully-coherent vacuum-GR analysis incurs a maximum one-dimensional (1D) systematic bias of ≈4.8σ from the truth, the shorter integration window of our loosely-coherent approach restricts such biases to ≲0.5σ. Overall, despite a fractional loss of optimal signal-to-noise ratio, the substantial improvement in parameter accuracy offered by our method presents a highly advantageous tradeoff for LISA and other future GW detectors.

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Phys. Rev. D 114, 104028– Published 6 November, 2026
  • Received 11 June 2026
  • Accepted 15 October 2026
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