Compact Binary Coalescences in Dense Gaseous Environments Can Pose as ones in Vacuum

TL;DR

This study demonstrates that compact binary coalescences in dense gaseous environments can produce gravitational-wave signals indistinguishable from vacuum events in detection but cause biases in parameter estimation, highlighting the need for environmental waveform models.

Contribution

The paper introduces numerical relativity simulations showing that dense gaseous environments can mimic vacuum signals and affect parameter estimation, emphasizing the importance of environmental waveform models.

Findings

Vacuum-like detection of events in dense environments

Environmental effects cause biases in parameter estimation

Phenomenological waveforms can detect and resolve environmental effects

Abstract

The gravitational-wave events observed by the LIGO-Virgo-KAGRA collaboration are attributed to compact binary coalescences happening in vacuum. However, several studies suggest that gaseous environments may play a significant role in the formation and evolution of compact binaries. Why have we not seen environmental effects in LVK signals? While matched-filtering remains the most effective technique for gravitational-wave searches, it comes with a burden: we might only observe signals that align with our (vacuum) expectations, potentially missing unexpected or unknown phenomena. Even more concerning is the possibility that environmental effects could mimic vacuum waveforms, introducing biases in parameter estimation and impacting population studies. Here, we use numerical relativity simulations of binary black hole mergers inside stellar envelopes to show that: (i) a $\texttt{GW150914}$-like event would be detected (with a false alarm rate smaller than $10^{-4}\, \mathrm{yr^{-1}}$) using a template bank of vacuum waveforms, even when immersed in a stellar envelope of density larger than $10^{7}\,\mathrm{g/cm^3}$; (ii) environmental effects can pass routinely performed tests of vacuum General Relativity, while leading to considerable biases in parameter estimation; but (iii) phenomenological environment waveforms are effectual in detecting environmental effects and can resolve systematics.