Inpainting allows recovery of pre-merger massive black hole binary signals in LISA data despite gaps and overlaps.
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A glitch-robust amortized inference framework combining normalizing flows, time-frequency multimodal fusion, and contrastive learning outperforms MCMC for Taiji massive black hole binary parameter estimation under noise contamination.
A neural spline flow pipeline performs amortized inference on millihertz MBHB signals, delivering ~20 deg² pre-merger sky localizations in ~1 minute while matching PTMCMC sky modes and parameter uncertainties.
LISA EMRIs can constrain deviations from Kerr equatorial symmetry to 10^{-2} and axial symmetry to 10^{-3} using Analytic Kludge waveforms and Fisher analysis.
citing papers explorer
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Inpainting over the cracks: challenges of applying pre-merger searches for massive black hole binaries to realistic LISA datasets
Inpainting allows recovery of pre-merger massive black hole binary signals in LISA data despite gaps and overlaps.
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Robust parameter inference for Taiji via time-frequency contrastive learning and normalizing flows
A glitch-robust amortized inference framework combining normalizing flows, time-frequency multimodal fusion, and contrastive learning outperforms MCMC for Taiji massive black hole binary parameter estimation under noise contamination.
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Pre-localization of Massive Black Hole Binaries in the Millihertz Band
A neural spline flow pipeline performs amortized inference on millihertz MBHB signals, delivering ~20 deg² pre-merger sky localizations in ~1 minute while matching PTMCMC sky modes and parameter uncertainties.
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Probing Kerr Symmetry Breaking with LISA Extreme-Mass-Ratio Inspirals
LISA EMRIs can constrain deviations from Kerr equatorial symmetry to 10^{-2} and axial symmetry to 10^{-3} using Analytic Kludge waveforms and Fisher analysis.