AI Insight
Researchers developed a machine learning framework using normalizing flows to rapidly estimate parameters of massive black hole binary mergers detectable by the LISA space observatory. The system, implemented in the DINGO code, can generate 20,000 posterior samples in under a minute and shows robust agreement with traditional methods for signals up to signal-to-noise ratios of approximately 500. At extremely high signal-to-noise ratios around 1,000, sampling efficiency decreases but still produces unbiased results that can serve as starting points for more detailed analysis.
Why it matters
This advancement could dramatically accelerate the analysis of gravitational wave signals from massive black hole mergers in space, enabling faster detection and characterization of these cosmic events. The method's speed makes it particularly valuable for time-sensitive applications like early-warning systems and rapid follow-up observations with other telescopes.
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⚠️ Preprint – Noch nicht peer-reviewed
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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 at fixed reference time. After sampling, we importance-sample to the true posterior based on the underlying likelihood and prior. 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 proposed 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.
Source: Accurate and efficient simulation-based inference for massive black-hole binaries with LISA