2026/07/31 by Hugo Einsle, Marie Anne Bizouard, Tania Regimbau +2
Physics and Astronomy · #gr-qc #astro-ph.HE #astro-ph.IM
arxiv created 2026/07/31 · arxiv updated 2026/08/04
Gravitational waves from compact binary coalescences and from early Universe processes are expected to form a gravitational-wave background. We employ a custom deep learning multi-scale multi-headed autoencoder architecture to isolate gravitational-wave background from detector noise, followed by a Markov chain Monte Carlo inference stage to separate the astrophysical and cosmological components. Analyzing 108-day mock datasets representative of the first period of the fourth LIGO-Virgo-KAGRA observing run, we show that we can detect with high confidence --- log10 noise Bayes factor larger than 3 --- a compact binary coalescence gravitational-wave background with an amplitude of 4.3+0.5-0.4×10-9 at f\rm ref=25 Hz, which is a factor ∼5 higher than the amplitude expected from compact binary sources. We also show that we can isolate a cosmological -- assumed flat spectrum -- gravitational-wave background as weak as 9.7+2.5-2.4 × 10-10 from the expected compact binary coalescence gravitational-wave background within simulated Gaussian noise mimicking the LIGO detectors sensitivity achieved in the fourth observing run. In blind-test comparisons with the standard pygwb pipeline, we show that our method achieves more accurate amplitude and spectral-index recovery and enables the separation of astrophysical and cosmological background components.