Uploaded July 2022 | Updated September 2026, 14 minutes ago
One of the most exciting gravitational wave source type LISA expects to detect are Extreme Mass-Ratio Inspirals (EMRIs). An EMRI is the slow inspiral of a small compact object (1 - 10 solar masses) into a massive black hole with mass range between 10^{4} and 10^{7} solar masses. EMRI waveforms are modelled using black-hole perturbation theory, where the background black-hole spacetime is perturbed by the self-force effects of the compact object’s gravitational field on its own orbit. However, the generation of EMRI waveforms to the degree of accuracy that is required for LISA science can be difficult to achieve, both theoretically and computationally. Therefore, LISA analysis will likely make use of approximate waveform models that are faster to generate at the expense of accuracy. Here we present a procedure for estimating the operational coverage of Bayesian credible sets for EMRI signal parameters that are inferred using approximate models. This allows us to quantify the approximation error and enables a subsequent adjustment of the nominal coverage levels.
Author and Presenter: Kate Lee
One of the most exciting gravitational wave source type LISA expects to detect are Extreme Mass-Ratio Inspirals (EMRIs). An EMRI is the slow inspiral of a small compact object (1 - 10 solar masses) into a massive black hole with mass range between 10^{4} and 10^{7} solar masses. EMRI waveforms are modelled using black-hole perturbation theory, where the background black-hole spacetime is perturbed by the self-force effects of the compact object’s gravitational field on its own orbit. However, the generation of EMRI waveforms to the degree of accuracy that is required for LISA science can be difficult to achieve, both theoretically and computationally. Therefore, LISA analysis will likely make use of approximate waveform models that are faster to generate at the expense of accuracy. Here we present a procedure for estimating the operational coverage of Bayesian credible sets for EMRI signal parameters that are inferred using approximate models. This allows us to quantify the approximation error and enables a subsequent adjustment of the nominal coverage levels.
Author and Presenter: Kate Lee
