  Strong lensing of gravitational waves can produce several detectable images
as repeated events in the upcoming observing runs, which can be detected with
the posterior overlap analysis (Bayes factor). The choice of the binary black
hole population plays an important role in the analysis as two
gravitational-wave events could be similar either because of lensing or
astrophysical coincidence. In this study, we investigate the biases induced by
different population models on the Bayes factor. We build up a mock catalogue
of gravitational-wave events following a benchmark population and reconstruct
it using both non-parametric and parametric methods. Using these
reconstructions, we compute the Bayes factor for lensed pair events by
utilizing both models and compare the results with a benchmark model. We show
that the use of a non-parametric population model gives a smaller bias than
parametric population models. Therefore, our study demonstrates the importance
of choosing a sufficiently agnostic population model for strong lensing
analyses.

General Relativity and Quantum Cosmology;Cosmology and Nongalactic Astrophysics;High Energy Physics - Phenomenology