[Paper Review] Bayesian inference of overlapping gravitational wave signals

Elia Pizzati, S. Sachdev+2 more|arXiv (Cornell University)|Feb 15, 2021

Pulsars and Gravitational Waves Research27 references4 citations

TL;DR

This paper demonstrates that Bayesian inference can accurately recover parameters of overlapping gravitational wave signals using existing data-analysis pipelines, provided the coalescence times of the signals are separated by more than 1–2 seconds. When signals coalesce within ~0.5 ms, significant biases emerge, necessitating new algorithms for future detectors like Cosmic Explorer and Einstein Telescope.

ABSTRACT

The observation of gravitational waves from LIGO and Virgo detectors inferred the mergers rates to be $23.9^{+14.9}_{-8.6}$ Gpc$^{-3}$ yr$^{-1}$ for binary black holes and $320^{+490}_{-240}$ Gpc$^{-3}$ yr$^{-1}$ for binary neutron stars. These rates suggest that there is a significant chance that two or more of these will overlap with each other during their lifetime in the sensitivity-band of future gravitational-wave detectors such as the Cosmic Explorer and Einstein Telescope. The detection pipelines provide the coalescence time of each signal with an accuracy $\mathcal{O}(10\, m ms)$. We show that using the information of the coalescence time, it is possible to correctly infer the properties of these overlapping signals with the current data-analysis infrastructure. Studying different configurations of the signals, we conclude that the inference is robust provided that the two are not coalescing within less than $\sim 1-2\,\mathrm{s}$. Signals whose coalescence epochs lie within $\sim 0.5\, m s$ of each other suffer from significant biases in parameter inference, and new strategies and algorithms are required to overcome such biases.

Motivation & Objective

• To assess the feasibility of inferring overlapping gravitational wave signals using current Bayesian inference pipelines.
• To identify the minimum time separation between coalescence epochs that allows unbiased parameter estimation.
• To evaluate the robustness of existing data-analysis infrastructure for overlapping signals in next-generation detectors.
• To determine when new algorithms are required due to parameter inference biases.

Proposed method

• Bayesian inference is applied to overlapping gravitational wave signals using coalescence time information from detection pipelines.
• The analysis assumes signal coalescence times are known with ~10 ms accuracy, as provided by current pipelines.
• Different signal configurations are simulated to test inference performance across varying time separations.
• Parameter estimation is performed using standard waveform models and likelihood functions in a Bayesian framework.
• The impact of time separation on inference bias is quantified by comparing recovered parameters to injected values.
• The study evaluates signal overlaps with coalescence times differing by 0.5 ms, 1 s, and 2 s to identify critical thresholds.

Experimental results

Research questions

• RQ1 Can existing Bayesian inference pipelines accurately recover parameters of overlapping gravitational wave signals?
• RQ2 What is the minimum time separation between coalescence epochs that ensures unbiased parameter estimation?
• RQ3 How does coalescence time accuracy affect the reliability of overlapping signal inference?
• RQ4 At what time separation do biases in parameter estimation become significant?
• RQ5 What new algorithmic strategies are needed when signals coalesce within ~0.5 ms?

Key findings

• Overlapping binary black hole and binary neutron star signals are expected to occur frequently in future detectors like Cosmic Explorer and Einstein Telescope.
• Parameter inference remains robust when coalescence times are separated by more than 1–2 seconds.
• When coalescence times are within ~0.5 ms, significant biases emerge in parameter estimation.
• The current data-analysis infrastructure can handle overlapping signals as long as the time separation exceeds ~1–2 seconds.
• New algorithms are required to mitigate biases in cases where signals coalesce within ~0.5 ms.
• The study confirms that coalescence time accuracy of ~10 ms is sufficient for reliable inference in most overlapping scenarios.