LUMA: A CNN for strong gravitational lens searches in astronomical imaging

Abstract

We present LUMA, a convolutional neural network (CNN) pipeline for the automated detection of strong gravitational lenses in simulated astronomical imaging. The method combines a physically motivated preprocessing stage, which enhances faint arc and ring features, with a compact three-block CNN trained using class reweighting and modern learning-rate scheduling. On simulated data, the model reaches test accuracies of about $96$\% and receiver operating characteristic (ROC) area-under-the-curve (AUC) values of $\simeq 0.99$ for the non-trivial classes, while confusion-matrix analysis shows high completeness and purity for lens candidates. These results demonstrate that relatively lightweight CNN architectures can provide a competitive baseline for strong-lens searches, and they motivate future extensions toward real survey images and transformer-based models.