Inferring additional physics through unmodelled signal reconstructions

TL;DR

This paper introduces a real-time waveform reconstruction pipeline that detects orbital eccentricity in gravitational wave signals, revealing biases in parameter estimation when eccentricity is ignored, and enabling low-latency astrophysical insights.

Contribution

The authors develop a minimally modelled waveform reconstruction method to identify eccentricity in gravitational wave data in real time, improving parameter estimation accuracy.

Findings

Ignoring eccentricity causes significant biases in parameter estimates.

Waveform reconstruction inconsistencies increase with eccentricity.

The method supports low-latency, targeted follow-up analyses.

Abstract

Parameter estimation of gravitational wave data is often computationally expensive, requiring simplifying assumptions such as circularisation of binary orbits. Although, if included, the sub-dominant effects like orbital eccentricity may provide crucial insights into the formation channels of compact binary mergers. To address these challenges, we present a pipeline strategy leveraging minimally modelled waveform reconstruction to identify the presence of eccentricity in real time. Using injected signals, we demonstrate that ignoring eccentricity ($e_{\rm 20Hz} \gtrsim 0.1$) leads to significant biases in parameter recovery, including chirp mass estimates falling outside the 90% credible interval. Waveform reconstruction shows inconsistencies increase with eccentricity, and this behaviour is consistent for different mass ratios. Our method enables low-latency inferences of binary properties supporting targeted follow-up analyses and can be applied to identify any physical effect of measurable strength.