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Beyond Gaussian assumptions: A new robust statistical framework for gravitational-wave data analysis

Argyro Sasli1,2,3, Minas Karamanis4, Nikolaos Karnesis3,5, Michael W. Coughlin1,2, Vuk Mandic1, Uroš Seljak4, and Nikolaos Stergioulas3

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Phys. Rev. D 114, 024004 – Published 1 July, 2026

DOI: https://doi.org/10.1103/n5vd-kzp1

Abstract

Many traditional algorithms applied in gravitational-wave astronomy rely on the assumption of Gaussian noise, a condition not always met in practice. Motivated by the need for analysis methods that are robust to such departures from Gaussianity, this study extends a robust statistical framework, advancing previous work on heavy-tailed likelihoods, that adapts the hyperbolic likelihood method for full frequency-domain applications. The framework is designed to maintain high performance under ideal conditions while improving robustness against non-Gaussian noise and outliers in real-world data. We demonstrate the efficacy of this approach through two key case studies. The first case study analyzes a massive black hole binary merger in simulated Laser Interferometer Space Antenna (LISA) data with Gaussian noise, showing that the extended hyperbolic likelihood method performs comparably to the more commonly used Whittle likelihood. The second case study examines a stellar-mass black hole binary merger using real ground-based gravitational-wave data containing non-Gaussian noise or overlapping signals, where our framework exhibits increased robustness and yields more accurate parameter estimations. Our results show that the hyperbolic likelihood better captures the true noise distribution, providing a flexible and physically motivated alternative for gravitational wave data analysis across current and future detectors.

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Phys. Rev. D 114, 024004– Published 1 July, 2026
  • Received 25 February 2026
  • Accepted 8 June 2026
Article part of CHORUS
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