Identifying when Precession can be Measured in Gravitational Waveforms

TL;DR

This paper systematically studies the parameter space where precession effects in gravitational waveforms from binary black holes can be accurately identified, using simulations and the precession SNR metric to inform detection prospects.

Contribution

It provides a quantitative analysis of how various binary parameters affect the measurability of precession in gravitational wave signals, introducing the precession SNR as a reliable metric.

Findings

Precession is more detectable with higher SNR, in-plane spins, unequal masses, and higher inclination.

The precession SNR correlates well with Bayesian evidence for precession.

Parameter dependencies on precession measurability are quantitatively characterized.

Abstract

In binary-black-hole systems where the black-hole spins are misaligned with the orbital angular momentum, precession effects leave characteristic modulations in the emitted gravitational waveform. Here, we investigate where in the parameter space we will be able to accurately identify precession, for likely observations over coming LIGO-Virgo-KAGRA observing runs. Despite the large number of parameters that characterise a precessing binary, we perform a large scale systematic study to identify the impact of each source parameter on the measurement of precession. We simulate a fiducial binary at moderate mass-ratio, signal-to-noise ratio (SNR), and spins, such that precession will be clearly identifiable, then successively vary each parameter while holding the remaining parameters fixed. As expected, evidence for precession increases with signal-to noise-ratio (SNR), higher in-plane spins, more unequal component masses, and higher inclination, but our study provides a quantitative illustration of each of these effects, and informs our intuition on parameter dependencies that have not yet been studied in detail, for example, the effect of varying the relative strength of the two polarisations, the total mass, and the aligned-spin components. We also measure the "precession SNR" $\rho_p$, which was introduced in Refs[1,2] to quantify the signal power associated with precession. By comparing $\rho_p$ with both Bayes factors and the recovered posterior distributions, we find it is a reliable metric for measurability that accurately predicts when the detected signal contains evidence for precession.