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With the upcoming large-scale surveys like LSST, we expect to findapproximately $10^5$ strong gravitational lenses among data of many orders ofmagnitude larger. In this scenario, the usage of non-automated techniques istoo time-consuming and hence impractical for science. For this reason, machinelearning techniques started becoming an alternative to previous methods. In ourprevious work, we proposed a new machine learning architecture based on theprinciple of self-attention, trained to find strong gravitational lenses onsimulated data from the Bologna Lens Challenge. Self-attention-based modelshave clear advantages compared to simpler CNNs and highly competing performancein comparison to the current state-of-art CNN models. We apply the proposedmodel to the Kilo Degree Survey, identifying some new strong lens candidates.However, these have been identified among a plethora of false positives, whichmade the application of this model not so advantageous. Therefore, throughoutthis paper, we investigate the pitfalls of this approach, and possiblesolutions, such as transfer learning, are proposed.

Transformers as Strong Lens Detectors- From Simulation to Surveys