Bayesian data augmentation reintroduces missing LISA data segments as auxiliary variables during posterior sampling to enable consistent parameter estimation for galactic binaries despite gaps.
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Numerical study with realistic LISA orbits and Bayesian methods finds that scalar ULDM signals can be discriminated from quasi-monochromatic GW signals.
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Gravitational-wave parameter estimation with gaps in LISA: a Bayesian data augmentation method
Bayesian data augmentation reintroduces missing LISA data segments as auxiliary variables during posterior sampling to enable consistent parameter estimation for galactic binaries despite gaps.
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Discriminating scalar ultralight dark matter from quasi-monochromatic gravitational waves in LISA
Numerical study with realistic LISA orbits and Bayesian methods finds that scalar ULDM signals can be discriminated from quasi-monochromatic GW signals.