Fully Data-Driven Time-Delay Interferometry with Time-Varying Delays

Raw space-based gravitational-wave data like laser interferometer space antenna's (LISA) phase measurements are dominated by laser frequency noise. The standard technique to make this data usable for gravitational-wave detection is time-delay interferometry (TDI), which cancels laser noise terms by forming suitable combinations of delayed measurements. To do so, TDI relies on inter-spacecraft distances and on how laser noise enters the interferometric data. The basic concepts of an alternative approach which does not rely on independent knowledge of temporal correlations in the dominant noise recently introduced. Instead, this automated principal component interferometry (aPCI) approach only assumes that one can produce some linear combinations of the temporally nearby regularly spaced phase measurements, which cancel the laser noise. Then the data is let to reveal those combinations, thus providing a set of laser-noise-free data channels. The authors' previous approach relies on the simplifying additional assumption that the filters which lead to the laser-noise-free data streams are time-independent. In LISA, however, these filters will vary as the constellation armlengths evolve. Here, a generalization of the basic aPCI concept compatible with data dominated by a still unmodeled but slowly varying dominant noise covariance is discussed. Despite its independence on any model, aPCI successfully mitigates laser frequency noise below the other noises' level, and its sensitivity to gravitational waves is the same as the state-of-the-art second-generation TDI, up to a 2% error.