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Premerger observation and characterization of massive black hole binaries

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arxiv 2411.07020 v1 pith:AQL5UG5B submitted 2024-11-11 hep-ex astro-ph.COgr-qc

classification hep-exastro-ph.COgr-qc
keywords blackearlylisamassivesignalsablebinarycharacterization
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We demonstrate an end-to-end technique for observing and characterizing massive black hole binary signals before they merge with the LISA space-based gravitational-wave observatory. Our method uses a zero-latency whitening filter, originally designed for rapidly observing compact binary mergers in ground-based observatories, to be able to observe signals with no additional latency due to filter length. We show that with minimal computational cost, we are able to reliably observe signals as early as 14 days premerger as long as the signal has accrued a signal-to-noise ratio of at least 8 in the LISA data. We also demonstrate that this method can be used to characterize the source properties, providing early estimates of the source's merger time, chirp mass, and sky localization. Early observation and characterization of massive black holes is crucial to enable the possibility of rapid multimessenger observations, and to ensure that LISA can enter a protected operating period when the merger signal arrives.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Accelerated Sequential Posterior Inference via Reuse for Gravitational-Wave Analyses

    hep-ex 2025-11 conditional novelty 6.0 of 10

    ASPIRE reuses old posterior samples via normalizing flows and sequential Monte Carlo to produce unbiased posteriors and evidences under new models, cutting likelihood evaluations 4-10x.

  2. A pipeline for searching and fitting instrumental glitches in LISA data

    gr-qc 2025-05 conditional novelty 6.0 of 10

    A reversible-jump MCMC pipeline simultaneously fits LISA instrumental glitches, noise, and a massive black hole binary signal, validated on simulated and modified Spritz challenge data.

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