2025/05/21 by Jun-Qian Jiang, Jiang, Jun-Qian, H. X. Huang +7
Earth and Planetary Sciences · Physics and Astronomy · #Cosmology and Gravitation Theories #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #FOS: Physical sciences #General Relativity and Quantum Cosmology (gr-qc) #Geophysics and Gravity Measurements #High Energy Physics - Theory (hep-th) #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Pulsars and Gravitational Waves Research
paper · pdf · doi:10.48550/arxiv.2505.15530
openalex publication_date 2025/05/21 · openalex created_date 2025/10/19 · openalex updated_date 2026/07/28
Primordial black holes (PBHs), envisioned as a compelling dark matter candidate and a window onto early-Universe physics, may contribute to some of the gravitational-wave (GW) signals detected by the LIGO-Virgo-KAGRA network. Traditional hierarchical Bayesian analysis, which relies on precise GW-event posterior estimates to extract information on potential PBH populations from GW events, becomes computationally demanding for catalogs with a large number of events. Here, we present a fast deep-learning framework, leveraging Transformer and normalizing flows, that maps GW-event posterior samples to joint posterior distributions over the hyperparameters of the PBH population. Our approach yields credible intervals with acceptable accuracy while delivering an order-of-magnitude speedup. These results highlight the potential of deep learning for fast and accurate PBH population studies, and its applicability to next-generation GW detectors when combined with appropriate event-level inference models.