2026/03/31 by Almat Akhmetali, Y. Sultan Abylkairov, Y. S. Abylkairov +11 · 1 citation
Computer Science · Physics and Astronomy · #Gaussian Processes and Bayesian Inference #Pulsars and Gravitational Waves Research #Statistical Mechanics and Entropy #astro-ph.HE
paper · pdf · doi:10.1103/h2b3-gdjx
published as Phys. Rev. D 114, 023053 (2026) · Published in Physical Review D
openalex publication_date 2026/06/16 · openalex created_date 2026/06/17 · openalex updated_date 2026/07/29 · arxiv created 2026/08/01 · arxiv updated 2026/08/04
Gravitational waves from core-collapse supernovae offer a unique probe of the equation of state (EOS) of dense nuclear matter. For rapidly rotating stars, previous machine-learning studies demonstrated promising EOS classification accuracy. However, these analyses relied on several simplifying assumptions. In this work, we relax three key assumptions. First, we include real detector noise. Second, we expand the analysis from a single progenitor model to four models spanning 12 to 40 solar masses, and for each mass we consider multiple rotational configurations, from slow to rapid. Third, we introduce uncertainty in the core bounce time of up to 20 ms, rather than assuming it is known precisely. We find that none of these effects significantly degrades EOS classification performance. Instead, the larger dataset associated with multiple progenitor models and noise realizations improves training and classification accuracy. This study is a step in a broader effort to progressively incorporate more realistic conditions into gravitational-wave inference for core-collapse supernovae.