Hierarchical data-driven modelling of binary Black hole mergers

Abstract

Current gravitational wave observations suggest that binary black hole (BBH) systems will be the dominant gravitational waves sources in the frequency range of advanced gravitational waves detectors. The full time-frequency dynamics of these systems have been long known to not be covered accurately by Post-Newtonian (PN) and Effective-OneBody (EOB) formulations of the two body problem. In particular, they fail to reproduce the merger-ringdown regimes where the strong general relativity (GR) effects arise. This involves that any of the quantities computed from the analytic approximants will suffer deviations that may induce an eventual loss of the signal-to-noise-ratio (SNR) and affect the parameter estimation (PE) results. On the other hand, numerical relativity (NR) simulations are thought to provide the most accurate representation of the full evolution thus filling the gap left by the analytic models. Current nonprecessing gravitational wave (GW) models are calibrated to NR data giving name to the so called inspiral-mergerringdown models (IMR) used in the LIGO template banks. Regarding the strategy they follow in describing the GW strain, they are classified as the EOBNR (time domain) and the Phenom-based models (frequency domain). Both approaches have been mainly calibrated with spin-aligned NR simulations, where the physical information is mostly described by means of the mass-ratio and some effective spin parameter. In this thesis we have developed the framework for a recalibration of the phenomenological models by adding a set of 23 unequal-spin NR simulations to include unequal-spin effects. To this end, we have created a novel fitting strategy that has been particularly well suited for the inclusion of the subdominant effects and the extreme mass-ratio regime. This new fit strategy has been used for the calibration of new and upgraded fits to the final spin, final mass and peak luminosity, being all of them used in the firsts LIGO GW observations. This fitting methodology is currently being tested and adapted for the recalibration of nonprecessing phenomenological models, also showing similar and promising results.