SPLIT: Loosely-coherent inference of long-inspiral gravitational-wave sources for robustness against modeling and data outliers
Abstract
The Laser Interferometer Space Antenna (LISA) will detect gravitational waves (GWs) from dozens of extreme- and intermediate-mass-ratio inspirals (EMRIs/IMRIs). 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 \textsc{SPLIT} (Segmented Posteriors for Long-Inspiral Templates), a GPU-accelerated Python package. Applying \textsc{SPLIT} to an environment-rich injection, we demonstrate that while a fully-coherent vacuum-GR analysis incurs a maximum 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 trade-off for LISA and other future GW detectors.
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BibTeX
@misc{kejriwal2026split,
title = {{SPLIT: Loosely-coherent inference of long-inspiral gravitational-wave sources for robustness against modeling and data outliers}},
author = {Shubham Kejriwal},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2606.13728},
eprint = {2606.13728},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.13728},
note = {CC BY-SA 4.0},
}
RIS
TY - UNPB
TI - SPLIT: Loosely-coherent inference of long-inspiral gravitational-wave sources for robustness against modeling and data outliers
AU - Shubham Kejriwal
PY - 2026
DA - 2026/10/18
PB - arXiv
DO - 10.48550/arxiv.2606.13728
UR - https://arxiv.org/abs/2606.13728
AB - The Laser Interferometer Space Antenna (LISA) will detect gravitational waves (GWs) from dozens of extreme- and intermediate-mass-ratio inspirals (EMRIs/IMRIs). 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 \textsc{SPLIT} (Segmented Posteriors for Long-Inspiral Templates), a GPU-accelerated Python package. Applying \textsc{SPLIT} to an environment-rich injection, we demonstrate that while a fully-coherent vacuum-GR analysis incurs a maximum 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 trade-off for LISA and other future GW detectors.
ER -