Overlapping signals in next-generation gravitational wave observatories: A recipe for selecting the best parameter estimation technique

TL;DR

This paper evaluates methods for parameter estimation of overlapping gravitational wave signals in next-generation detectors, proposing a new bias estimation approach and assessing existing techniques' reliability and accuracy.

Contribution

It introduces a reweighting-based bias estimation method for overlapping signals and compares multiple existing techniques for their effectiveness in future gravitational wave observatories.

Findings

Time-frequency overlap method is 86% accurate for close binary black hole mergers.

Prior-informed Fisher matrix is unreliable despite improvements.

Reweighting posterior estimates achieves 99% accuracy in bias estimation.

Abstract

Third-generation gravitational wave detectors such as Einstein Telescope and Cosmic Explorer will have significantly better sensitivities than current detectors, as well as a wider frequency bandwidth. This will increase the number and duration of the observed signals, leading to many signals overlapping in time. If not adequately accounted for, this can lead to biases in parameter estimation. In this work, we combine the joint parameter estimation method with relative binning to handle full parameter inference on overlapping signals from binary black holes, including precession effects and higher-order mode content. As this method is computationally more expensive than traditional single-signal parameter estimation, we test a prior-informed Fisher matrix and a time-frequency overlap method for estimating expected bias to help us decide when joint parameter estimation is necessary over the simpler methods. We improve upon previous Fisher matrix implementations by including the prior information and performing an optimization routine to better locate the maximum likelihood point point, but we still find the method unreliable. The time-frequency method is accurate in 86% of close binary black hole mergers. We end by developing our own method of estimating bias due overlaps, where we reweight the single signal parameter estimation posterior to quantify how much the overlapping signals affect it. We show it has 99% accuracy for zero noise injections (98% in Gaussian noise), at the cost of one additional standard sampling run when joint parameter estimation proves to be necessary.