Properties of neural networks identifying strongly lensed gravitational waves in time domain

Just as light, gravitational waves (GWs) can also be lensed. In the case of strong lensing, one would observe multiple copies of the same initial wave with different amplitudes, arrival times and possibly phases. As the GW detection rate increases with improving detectors, it will become more and more difficult for the analyses searching for strong-lensing signatures to keep up. Machine learning models were proposed to address this issue. In this work, we present the performance of an attention-based neural network (AttNN) trained to identify strongly lensed pairs of GWs in the time domain. We also investigate its properties and those of a convolutional neural network (CNN) to identify their advantages and limitations.This article is part of the Theo Murphy meeting issue 'Multi-messenger gravitational lensing (Part 1)'.