Accurate and efficient simulation-based inference for massive black-hole binaries with LISA
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911532775899136 |
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| author | Spadaro, Alice Gair, Jonathan Gerosa, Davide Green, Stephen R. Buscicchio, Riccardo Gupte, Nihar Tenorio, Rodrigo Clyne, Samuel Pürrer, Michael Korsakova, Natalia |
| author_facet | Spadaro, Alice Gair, Jonathan Gerosa, Davide Green, Stephen R. Buscicchio, Riccardo Gupte, Nihar Tenorio, Rodrigo Clyne, Samuel Pürrer, Michael Korsakova, Natalia |
| contents | We develop an accurate simulation-based inference framework for high-mass ($\gtrsim\!10^7 \rm{M_\odot}$) black-hole binaries observable by LISA. The method is implemented within the DINGO gravitational-wave parameter-estimation code, extending its application from ground-based detectors to the LISA band. We train a normalizing-flow model using aligned-spin higher-mode waveform models and a low-frequency approximation of the detector response. After sampling, we importance-sample to the true posterior. We validate performance on simulated signals spanning the signal-to-noise regimes relevant for LISA observations and benchmark our new DINGO implementation against standard methods. We report robust agreement in the inferred posterior distributions up to signal-to-noise ratios of $\sim\!500$. At higher signal-to-noise ratios of $\sim\!1000$, we observe a reduction in sampling efficiency, while still yielding unbiased and tightly localized posteriors that can be used as a starting point for follow-up with traditional methods.The trained flow can generate 20 thousand posterior samples in less than a minute, establishing DINGO as a promising neural inference framework for rapid full-parameter estimation of massive black-hole binaries in the LISA band. The likelihood-free nature of this approach allows for straightforward generalizations, including a time-dependent detector response, non-stationary noise artifacts such as gaps and glitches, and low-latency parameter estimations. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_20431 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Accurate and efficient simulation-based inference for massive black-hole binaries with LISA Spadaro, Alice Gair, Jonathan Gerosa, Davide Green, Stephen R. Buscicchio, Riccardo Gupte, Nihar Tenorio, Rodrigo Clyne, Samuel Pürrer, Michael Korsakova, Natalia High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics General Relativity and Quantum Cosmology We develop an accurate simulation-based inference framework for high-mass ($\gtrsim\!10^7 \rm{M_\odot}$) black-hole binaries observable by LISA. The method is implemented within the DINGO gravitational-wave parameter-estimation code, extending its application from ground-based detectors to the LISA band. We train a normalizing-flow model using aligned-spin higher-mode waveform models and a low-frequency approximation of the detector response. After sampling, we importance-sample to the true posterior. We validate performance on simulated signals spanning the signal-to-noise regimes relevant for LISA observations and benchmark our new DINGO implementation against standard methods. We report robust agreement in the inferred posterior distributions up to signal-to-noise ratios of $\sim\!500$. At higher signal-to-noise ratios of $\sim\!1000$, we observe a reduction in sampling efficiency, while still yielding unbiased and tightly localized posteriors that can be used as a starting point for follow-up with traditional methods.The trained flow can generate 20 thousand posterior samples in less than a minute, establishing DINGO as a promising neural inference framework for rapid full-parameter estimation of massive black-hole binaries in the LISA band. The likelihood-free nature of this approach allows for straightforward generalizations, including a time-dependent detector response, non-stationary noise artifacts such as gaps and glitches, and low-latency parameter estimations. |
| title | Accurate and efficient simulation-based inference for massive black-hole binaries with LISA |
| topic | High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics General Relativity and Quantum Cosmology |
| url | https://arxiv.org/abs/2603.20431 |