On modeling for Kerr black holes: Basis learning, QNM frequencies, and spherical-spheroidal mixing coefficients
L. London, E. Fauchon-Jones

TL;DR
This paper introduces new phenomenological models for Kerr black hole gravitational wave spectra, focusing on quasi-normal mode frequencies and harmonic mixing coefficients, using innovative regression methods that account for black hole spin variations.
Contribution
It presents novel regression-based models for QNM frequencies and harmonic mixing coefficients, explicitly incorporating black hole spin effects and extending previous results.
Findings
Models are the first to consider spin from -1 to 1.
GMVR method effectively interpolates rational functions.
Models connect pro and retrograde modes across spin range.
Abstract
Models of black hole properties play an important role in the ongoing direct detection of gravitational waves from black hole binaries. One important aspect of model based gravitational wave detection, and subsequent estimation of source parameters, are the low level modeling of information related to perturbed Kerr black holes. Here, we present new phenomenological methods to model the analytically understood gravitational wave spectra (quasi-normal mode frequencies), and harmonic structure of Kerr black holes (mixing coefficients between spherical and spheroidal harmonics). In particular, we present a greedy-multivariate-polynomial (GMVP) regression method and greedy-multivariate-rational (GMVR) regression method for the automated modeling of polynomial and rational functions respectively. GMVR is a quasi-linear numerical method for interpolating rational functions. It therefore…
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