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Abstract

By listening to gravity in the low frequency band, between 0.1 mHz and 1 Hz,
the future space-based gravitational-wave observatory LISA will be able to
detect tens of thousands of astrophysical sources from cosmic dawn to the
present. The detection and characterization of all resolvable sources is a
challenge in itself, but LISA data analysis will be further complicated by
interruptions occurring in the interferometric measurements. These
interruptions will be due to various causes occurring at various rates, such as
laser frequency switches, high-gain antenna re-pointing, orbit corrections, or
even unplanned random events. Extracting long-lasting gravitational-wave
signals from gapped data raises problems such as noise leakage and increased
computational complexity. We address these issues by using Bayesian data
augmentation, a method that reintroduces the missing data as auxiliary
variables in the sampling of the posterior distribution of astrophysical
parameters. This provides a statistically consistent way to handle gaps while
improving the sampling efficiency and mitigating leakage effects. We apply the
method to the estimation of galactic binaries parameters with different gap
patterns, and we compare the results to the case of complete data.