Strong lenses KiDS DR4

Fine-tuning the transformer encoder to the KiDS data reduced the number of false positives by 70%. Additionally, applying the fine-tuned model to a sample of ~5000000 galaxies resulted in a list of ~51000 SGL candidates. Upon visual inspection, this list was narrowed down to 231 candidates.

Combined with the SGL candidates identified in the model testing, our final sample comprises 264 candidates, including 71 high-confidence SGLs; of these 71, 44 are new discoveries. We propose fine-tuning via real augmented images as a viable approach to mitigating false positives when transitioning from simulated lenses to real surveys. While our model shows improvement, it still does not achieve the same accuracy as previously proposed models trained directly on galaxy images from KiDS with added simulated lensing arcs.

This suggests that a larger fine- tuning set is necessary for a competitive performance. Additionally, we provide a list of 121 false positives that exhibit features similar to lensed objects, which can be used in the training of future machine learning models in this field.