A $\chi^2$ statistic for the identification of strongly lensed gravitational waves from compact binary coalescences

TL;DR

This paper introduces a new chi-squared statistic to efficiently distinguish strongly lensed gravitational wave signals from unlensed ones, aiding rapid identification in upcoming GW observations.

Contribution

A novel chi-squared based statistic tailored for identifying lensed GW signals, offering comparable accuracy to Bayesian and machine learning methods but with significantly faster computation.

Findings

Achieves high lensed event detection efficiency

Faster than Bayesian methods for large datasets

Fully characterized in Gaussian noise environments

Abstract

Gravitational waves (GWs) emanated by stellar mass compact binary coalescences (CBCs), and lensed by galaxy- or cluster-scale lenses, will produce two or more copies of the GW signal. These will have identical phase evolution but differing amplitudes. Such lensing signatures are expected to be detected by the end of the LIGO-Virgo-Kagra's (LVK's) fifth observing run (O5). In this work, we propose a novel $\chi_{\mathrm{lens}}^2$ statistic to segregate pairs of detected GW events as either lensed or unlensed, using templates typically used in GW searches. The statistic is an application of the generalized $\chi^2$ discriminator described in \citet{dhurandhar2017}, tailored to probe the similarity (or lack thereof) between the phase evolutions of two CBC signals. We assess the performance of $\chi_{\mathrm{lens}}^2$ on a realistic astrophysical dataset of lensed and unlensed CBCs detectable in O4, assuming a single LIGO-like detector at design sensitivity. We find that we can correctly identify lensed events with efficiencies comparable to existing Bayesian and machine learning methods. Evaluating $\chi_{\mathrm{lens}}^2$ is orders of magnitude faster than Bayesian methods. Moreover, the statistics of $\chi_{\mathrm{lens}}^2$, in stationary Gaussian noise, are fully understood, in contrast to machine learning methods. $\chi_{\mathrm{lens}}^2$ can, therefore, be used to rapidly and accurately weed out the vast majority of unlensed candidate pairs and identify lensed pairs.