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Life sciences · Preprint
arXiv · September 10, 2026
Posted before peer review. The findings may change or fail to hold.
This preprint describes a weighted conformal prediction framework designed to improve gravitational wave detection sensitivity by combining multiple search pipelines and correcting for covariate shift between training and real data. Testing is performed only on simulated datasets; practical validation on real detector observations and peer review are pending.
Simulation-based methods development study. Intervention: Weighted conformal prediction framework combining multiple search pipelines with likelihood-ratio reweighting for covariate shift correction. Compared with: Standard approach of selecting the most significant single pipeline output.
Weighted conformal prediction restores well-calibrated coverage under covariate shift in mock datasets Method increases confidence of events near detection threshold Framework recovers true signals that would otherwise be missed using standard pipeline selection
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This is an unrefereed methodological preprint describing a statistical framework for gravitational wave detection; it presents algorithm development with simulated data but has not undergone peer review.
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In the last decade, kilometre-scale interferometric gravitational-wave detectors have observed hundreds of compact binary mergers, the majority of which are binary black holes. However, the data are noise-dominated, and multiple independent search algorithms (pipelines) are used to enhance sensitivity and improve robustness. Rather than the standard approach of selecting the most significant pipeline output, we combine the outputs from all pipelines using a conformal prediction-based framework to provide statistically rigorous confidence estimates for candidate events. While combining pipelines improves sensitivity and ranking robustness, it requires a principled statistical framework that remains valid as data properties evolve across observing runs. A key challenge is distribution shifts between simulated datasets used for training and calibration and the real, unlabelled, observations used for testing, which can invalidate coverage guarantees and bias confidence estimates. In this work, we address this challenge by incorporating likelihood-ratio reweighting into our conformal prediction framework to account for covariate shift. Using mock datasets containing simulated signals, we demonstrate that weighted conformal prediction restores well-calibrated coverage under covariate shift and increases the confidence of events near the detection threshold, recovering true signals that would otherwise be missed.
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