Speaker
Description
When a gravitational wave signal encounters a massive object, such as a galaxy or galaxy cluster, it undergoes strong gravitational lensing, producing multiple copies of the original signal. These strongly lensed signals share identical waveform morphology in the frequency domain, enabling analysis without complex lens models. However, stellar fields and dark matter substructures within the galactic lens introduce microlensing effects that distort individual signals, making their identification computationally challenging within Bayesian frameworks. In this study, we propose a novel residual test to efficiently detect microlensing signatures by leveraging the fact that current Bayesian inference pipelines are optimized for the strong lensing hypothesis. Using cross-correlation techniques, we analyze microlensing-induced deviations imprinted in the residuals. Our simulations based on realistic microlensing populations show that while most events exhibit minor mismatches, a fraction have significant deviations. We find that 29% (56%) and 37% (69%) of microlensed events with mismatch > 0.03 and > 0.1, respectively, can be identified with O4 (O5) detector sensitivities, indicating that high-mismatch events are more likely to be discerned as microlensed. Considering the full population, our approach enables the identification of 12% (21.5%) of microlensed events with the O4 (O5) sensitivity.
