Residual eccentricity as a systematic uncertainty on the formation channels of binary black holes

TL;DR

Residual orbital eccentricity, even below current detection thresholds, can bias the inference of binary black hole formation channels, especially affecting spin orientation estimates in gravitational-wave observations.

Contribution

This study quantifies how undetectable residual eccentricity influences the inference of black hole binary formation pathways, highlighting it as a systematic uncertainty.

Findings

Eccentricities as low as 10^{-4} can bias spin orientation inference.

Residual eccentricity acts as a systematic uncertainty in astrophysical inference.

Marginalizing over eccentricity can mitigate biases.

Abstract

Resolving the formation channel(s) of merging binary black holes is a key goal in gravitational-wave astronomy. The orbital eccentricity is believed to be a precious tracer of the underlying formation pathway, but is largely dissipated during the usually long inspiral between black hole formation and merger. Most gravitational-wave sources are thus expected to enter the sensitivity windows of current detectors on configurations that are compatible with quasi-circular orbits. In this paper, we investigate the impact of "negligible" residual eccentricity -- lower than currently detectable by LIGO/Virgo -- on our ability to infer the formation history of binary black holes, focusing in particular on their spin orientations. We trace the evolution of both observed and synthetic gravitational-wave events backward in time, while resampling their residual eccentricities to values that are below the detectability threshold. Eccentricities in-band as low as $\sim 10^{-4}$ can lead to significant biases when reconstructing the spin directions, especially in the case of loud, highly precessing systems. Residual eccentricity thus act like a systematic uncertainty for our astrophysical inference. As a mitigation strategy, one can marginalize the posterior distribution over the residual eccentricity using astrophysical predictions.