A fast deep-learning approach to probing primordial black hole populations in gravitational wave events
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909618743017472 |
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| author | Jiang, Jun-Qian Huang, Hai-Long He, Jibin Wang, Yu-Tong Piao, Yun-Song |
| author_facet | Jiang, Jun-Qian Huang, Hai-Long He, Jibin Wang, Yu-Tong Piao, Yun-Song |
| contents | Primordial black holes (PBHs), envisioned as a compelling dark matter candidate and a window onto early-Universe physics, may contribute to the part 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, for extracting the information of potential PBH population from GW events become computationally prohibitive for catalogs of hundreds 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 accurate credible intervals while reducing end-to-end inference time to $\mathcal{O}(1)$ s on a single GPU. These results underscore the potential of deep learning for fast, high-accurately PBH population studies in the era of next-generation GW detectors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_15530 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A fast deep-learning approach to probing primordial black hole populations in gravitational wave events Jiang, Jun-Qian Huang, Hai-Long He, Jibin Wang, Yu-Tong Piao, Yun-Song General Relativity and Quantum Cosmology Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics High Energy Physics - Theory Primordial black holes (PBHs), envisioned as a compelling dark matter candidate and a window onto early-Universe physics, may contribute to the part 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, for extracting the information of potential PBH population from GW events become computationally prohibitive for catalogs of hundreds 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 accurate credible intervals while reducing end-to-end inference time to $\mathcal{O}(1)$ s on a single GPU. These results underscore the potential of deep learning for fast, high-accurately PBH population studies in the era of next-generation GW detectors. |
| title | A fast deep-learning approach to probing primordial black hole populations in gravitational wave events |
| topic | General Relativity and Quantum Cosmology Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics High Energy Physics - Theory |
| url | https://arxiv.org/abs/2505.15530 |