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  • Open Access

Enhancing Gravitational-Wave Detection: A Machine Learning Pipeline Combination Approach with Robust Uncertainty Quantification

Gregory Ashton1,*, Ann-Kristin Malz1, and Nicolo Colombo2

  • *Contact author: gregory.ashton@ligo.org
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Phys. Rev. Lett. 136, 011402 – Published 8 January, 2026

DOI: https://doi.org/10.1103/yfb3-fgf2

Abstract

Gravitational-wave data from advanced-era interferometric detectors consists of background Gaussian noise, frequent transient artifacts, and rare astrophysical signals. Multiple search algorithms exist to detect the signals from compact binary coalescences, but their varying performance complicates interpretation. We present a machine-learning-driven approach that combines results from individual pipelines and utilizes conformal prediction to provide robust, calibrated uncertainty quantification. Using simulations, we demonstrate improved detection efficiency and apply our model to GWTC-3, enhancing confidence in multipipeline detections, such as the subthreshold binary neutron star candidate GW200311_103121.

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Phys. Rev. Lett. 136, 011402– Published 8 January, 2026
  • Received 25 April 2025
  • Accepted 4 December 2025
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

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