We extend the Ori and Thorne (OT) procedure to compute the transition from an adiabatic inspiral into a geodesic plunge for any spin. Our analysis revisits the validity of the approximations made in OT. In particular, we discuss possible effects coming from eccentricity and non-geodesic past-history of the orbital evolution. We find three different scaling regimes according to whether the mass ratio is much smaller, of the same order or much larger than the near extremal parameter describing how fast the primary black hole rotates. Eccentricity and non-geodesic past-history corrections are always sub-leading, indicating that the quasi-circular approximation applies throughout the transition regime. However, we show that the OT assumption that the energy and angular momentum evolve linearly with proper time must be modified in the near-extremal regime. Using our transition equations, we describe an algorithm to compute the full worldline in proper time for an extreme mass ratio inspiral (EMRI) and the full gravitational waveform in the high spin limit.
Understanding and dealing with inference biases in gravitational-wave (GW) parameter estimation when a plethora of signals are present in the data is one of the key challenges for the analysis of data from future GW detectors. Working within the linear signal approximation, we describe generic metrics to predict inference biases on GW source parameters in the presence of confusion noise from unfitted foregrounds, from overlapping signals that coalesce close in time to one another, and from residuals of other signals that have been incorrectly fitted out. We illustrate the formalism with simplified, yet realistic, scenarios appropriate to third-generation ground-based (Einstein Telescope) and space-based (LISA) detectors, and demonstrate its validity against Monte-Carlo simulations. We find it to be a reliable tool to cheaply predict the extent and direction of the biases. Finally, we show how this formalism can be used to correct for biases that arise in the sequential characterisation of multiple sources in a single data set, which could be a valuable tool to use within a global-fit analysis pipeline.
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