Kavli Affiliate: Wayne Hu
| First 5 Authors: Giulia Campailla, Giulia Campailla, , ,
| Summary:
When a gravitational wave encounters a massive object along the line of
sight, repeated copies of the original signal may be produced due to
gravitational lensing. In this paper, we develop a series of new
machine-learning based statistical methods to identify promising strong lensing
candidates in gravitational wave catalogs. We employ state-of-the-art
normalizing flow generative models to perform statistical calculations on the
posterior distributions of gravitational wave events that would otherwise be
computationally unfeasible. Our lensing identification strategy, developed on
two simulated gravitational wave catalogs that test noise realization and event
signal variations, selects event pairs with low parameter differences in the
optimal detector basis that also have a high information content and favorable
likelihood for coincident parameters. We then apply our method to the GWTC-3
catalog and find a single pair still consistent with the lensing hypothesis.
This pair has been previously identified through more costly evidence ratio
techniques, but rejected on astrophysical grounds, which further validates our
technique.
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