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| Autori principali: | , , , |
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| Natura: | Preprint |
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2025
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| Accesso online: | https://arxiv.org/abs/2508.16399 |
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| _version_ | 1866918486232530944 |
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| author | Singh, Shashwat Chapman-Bird, Christian E. A. Berry, Christopher P L Veitch, John |
| author_facet | Singh, Shashwat Chapman-Bird, Christian E. A. Berry, Christopher P L Veitch, John |
| contents | Gravitational waves from extreme mass-ratio inspirals (EMRIs), the inspirals of stellar-mass compact objects into massive black holes, are predicted to be observed by the Laser Interferometer Space Antenna (LISA). A sufficiently large number of EMRI observations will provide unique insights into the massive black hole population. We have developed a hierarchical Bayesian inference framework capable of constraining the parameters of the EMRI population, accounting for selection biases. We leverage the capacity of a feed-forward neural network as an emulator, enabling detectability calculations of $\sim10^5$ EMRIs in a fraction of a second, speeding up the likelihood evaluation by $\gtrsim6$ orders of magnitude. We validate our framework on a phenomenological EMRI population model. This framework enables studies of how well we can constrain EMRI population parameters, such as the slope of both the massive and stellar-mass black hole mass spectra and the branching fractions of different formation channels, allowing further investigation into the evolution of massive black holes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16399 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Constraints on the extreme mass-ratio inspiral population from LISA data Singh, Shashwat Chapman-Bird, Christian E. A. Berry, Christopher P L Veitch, John General Relativity and Quantum Cosmology High Energy Astrophysical Phenomena Gravitational waves from extreme mass-ratio inspirals (EMRIs), the inspirals of stellar-mass compact objects into massive black holes, are predicted to be observed by the Laser Interferometer Space Antenna (LISA). A sufficiently large number of EMRI observations will provide unique insights into the massive black hole population. We have developed a hierarchical Bayesian inference framework capable of constraining the parameters of the EMRI population, accounting for selection biases. We leverage the capacity of a feed-forward neural network as an emulator, enabling detectability calculations of $\sim10^5$ EMRIs in a fraction of a second, speeding up the likelihood evaluation by $\gtrsim6$ orders of magnitude. We validate our framework on a phenomenological EMRI population model. This framework enables studies of how well we can constrain EMRI population parameters, such as the slope of both the massive and stellar-mass black hole mass spectra and the branching fractions of different formation channels, allowing further investigation into the evolution of massive black holes. |
| title | Constraints on the extreme mass-ratio inspiral population from LISA data |
| topic | General Relativity and Quantum Cosmology High Energy Astrophysical Phenomena |
| url | https://arxiv.org/abs/2508.16399 |