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Accurate and efficient simulation-based inference for massive black-hole binaries with LISA

A Spadaro, J Gair, D Gerosa, SR Green… - arXiv preprint arXiv …, 2026 - arxiv.org
Astrophysics paper astro-ph.HE Suggest

… We develop an accurate simulation-based inference framework for high-mass (≳107M⊙) black-hole binaries observable by LISA. The method is implemented within the …

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@article{2603.20431v1,
Author        = {Alice Spadaro and Jonathan Gair and Davide Gerosa and Stephen R. Green and Riccardo Buscicchio and Nihar Gupte and Rodrigo Tenorio and Samuel Clyne and Michael Pürrer and Natalia Korsakova},
Title         = {Accurate and efficient simulation-based inference for massive black-hole binaries with LISA},
Eprint        = {2603.20431v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.HE},
Abstract      = {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.},
Year          = {2026},
Month         = {Mar},
Url           = {http://arxiv.org/abs/2603.20431v1},
File          = {2603.20431v1.pdf}
}

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