2024/10/18 by Nikolaos Karnesis, Karnesis, Nikolaos, A. Sasli +5 · 1 citation
Computer Science · Decision Sciences · #Advanced Statistical Process Monitoring #Blind Source Separation Techniques #Distributed Sensor Networks and Detection Algorithms #FOS: Physical sciences #General Relativity and Quantum Cosmology (gr-qc) #Instrumentation and Methods for Astrophysics (astro-ph.IM)
paper · pdf · doi:10.48550/arxiv.2410.14354
openalex publication_date 2024/10/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Future Gravitational Wave observatories will give us the opportunity to search for stochastic signals of astrophysical, or even cosmological origins. However, parameter estimation and search will be challenging, mostly due to the overlap of multiple signal components, as well as the potentially partially unknown properties of the instrumental noise. In this work, we propose a robust statistical framework based on heavier-tailed likelihoods for the characterization of stochastic gravitational-wave signals. In particular, we use the symmetric hyperbolic likelihood, which allows us to probe the signal spectral properties and simultaneously test for any departures from Gaussianity. We demonstrate this methodology with synthetic data from the future LISA mission, where we estimate the potential non-Gaussianities induced by the unresolved Ultra Compact Galactic Binaries.