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Parameter Estimation for Stellar-Origin Black Hole Mergers In LISA

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arxiv 2212.04600 v1 pith:7TIVIQEM submitted 2022-12-08 gr-qc astro-ph.HEastro-ph.IM

Matthew C. Digman , Neil J. Cornish This is my paper · ORCID
classification gr-qcastro-ph.HEastro-ph.IM
keywords lisadetectorsground-basedsobhbsbeforeblackdetectedestimation
verification ladder T0 review T1 audit T2 compute T3 formal
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The population of stellar origin black hole binaries (SOBHBs) detected by existing ground-based gravitational wave detectors is an exciting target for the future space-based Laser Interferometer Space Antenna (LISA). LISA is sensitive to signals at significantly lower frequencies than ground-based detectors. SOBHB signals will thus be detected much earlier in their evolution, years to decades before they merge. The mergers will then occur in the frequency band covered by ground-based detectors. Observing SOBHBs years before merger can help distinguish between progenitor models for these systems. We present a new Bayesian parameter estimation algorithm for LISA observations of SOBHBs that uses a time-frequency (wavelet) based likelihood function. Our technique accelerates the analysis by several orders of magnitude compared to the standard frequency domain approach and allows for an efficient treatment of non-stationary noise.

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Cited by 4 Pith papers

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

  1. Multiband parameter estimation with phase coherence and extrinsic marginalization: Extracting more information from low-SNR CBC signals in LISA data

    gr-qc 2025-06 conditional novelty 8.0 of 10

    A coherent multiband Bayesian parameter estimation method with extrinsic-parameter marginalization extracts useful information from LISA observations of stellar-mass binary black holes down to LISA SNR 3, nearly doubl...

  2. Non-stationary noise in gravitational wave analyses: The wavelet domain noise covariance matrix

    gr-qc 2025-11 conditional novelty 7.0 of 10

    For slowly varying detector noise, the Wilson-Daubechies-Meyer wavelet noise covariance matrix is approximately diagonal, with off-diagonal terms controlled by the time and frequency derivatives of the dynamic spectral model.

  3. Enhancing Taiji's Parameter Estimation under Non-Stationarity: a Time-Frequency Domain Framework for Galactic Binaries and Instrumental Noises

    gr-qc 2025-06 conditional novelty 7.0 of 10

    A time-frequency (STFT) Bayesian framework improves Taiji Galactic binary and noise parameter estimation under non-stationary noise compared with frequency-domain analysis.

  4. An explicit and differentiable Wilson-Daubechies-Meyer transform for gravitational-wave data analysis

    gr-qc 2026-06 unverdicted novelty 4.0 of 10

    Open-source WDM transform package with JAX support and numerical validation of equivalence to frequency-domain likelihoods for a LISA binary under stationary noise.

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