The Cost of Circularity: Quantifying Eccentricity-Induced Biases in Binary Black Hole Inference

TL;DR

This paper investigates how unmodeled orbital eccentricity in binary black hole signals biases parameter estimation, showing that neglecting eccentricity leads to significant inaccuracies for eccentricities above 0.2.

Contribution

It quantifies the impact of eccentricity on black hole merger parameter inference and identifies when eccentric waveform models are necessary for accurate results.

Findings

Circular models are reliable only for very small eccentricities.

Eccentricities above 0.2 cause significant biases in inferred parameters.

Circular templates can mimic eccentric effects, leading to degeneracies.

Abstract

Dynamically assembled binary black holes are expected to retain measurable orbital eccentricity in the LIGO-Virgo-KAGRA band, but most parameter estimation analyses still assume quasi-circular inspirals. This raises a critical question: how strongly does unmodeled eccentricity bias the inferred properties of BBH mergers? We address this by injecting eccentric signals generated with TEOBResumS-Dali and recovering them using the circular, precessing IMRPhenomXPHM waveform model. Across $20$-$80 \, M_\odot$ and eccentricities up to $e=0.5$, we find that circular waveform models remain reliable only for very small eccentricities. Above $e\sim0.2$ at 10 Hz, recovered masses, spins, inclination, and distances begin to show significant systematic offsets. Circular precessing templates mimic eccentric amplitude and phase modulations by introducing artificial precession, highlighting a major degeneracy between these effects. For high-mass, moderately eccentric mergers, circular models misestimate parameters at a level that would bias astrophysical interpretation and population studies. Our results establish the parameter-space boundaries where eccentric waveform models become essential for accurate inference in current and next-generation detectors.