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A rapid method for preliminary identification of subthreshold strongly lensed counterparts to superthreshold gravitational-wave events
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Gravitational waves (GWs) from stellar-mass compact binary coalescences (CBCs) are expected to be strongly lensed when encountering large agglomerations of matter, such as galaxies or clusters. Searches for strongly lensed GWs have been conducted using data from the first three observing runs of the LIGO-Virgo GW detector network. Although no confirmed detections have been reported, interesting candidate lensed pairs have been identified. In this work, we delineate a preliminary analysis that rapidly identifies pairs to be further analyzed by more sophisticated Bayesian parameter estimation (PE) methods. The analysis relies on the Gaussian/Fisher approximation to the likelihood and compares the corresponding approximate posteriors on the chirp masses of the candidate pair. It additionally cross-correlates the rapidly produced localization sky areas (constructed by Bayestar sky-localization software). The analysis was used to identify pairs involving counterparts from targeted sub-threshold searches to confidently detected super-threshold CBC events. The most significant candidate ``super-sub'' pair deemed by this analysis was subsequently found, by more sophisticated and detailed joint-PE analyses, to be among the more significant candidate pairs, but not sufficiently significant to suggest the observation of a lensed event [1].
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Cited by 3 Pith papers
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Identifying lensed gravitational waves with physics-informed posterior learning
Fusing a simulation-trained common-source mass posterior with waveform features raises lensed-event detection efficiency from 20.8% to 35.2% at 1% false-positive rate and lowers the SNR for 50% efficiency from 45.3 to 33.5.
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A $\chi^2$ statistic for the identification of strongly lensed gravitational waves from compact binary coalescences
A chi-squared test built from gravitational wave search templates can separate lensed event pairs from unlensed pairs with accuracy comparable to slower Bayesian methods.
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Machine Learning Assisted Parameter-Space Searches for Lensed Gravitational Waves
Normalizing flow based non-Gaussian consistency tests in a compressed detector basis select GW170104-GW170814 as the only promising lensed pair in GWTC-3.
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