LenNet: direct detection and localization of strong gravitational lenses in wide-field sky survey images

Strong gravitational lenses are invaluable for tackling fundamental astrophysics questions, such as the nature of dark matter and cosmic expansion. However, current sky surveys’ “crop-and-classify” lens search method faces a critical challenge: it creates massive computational and storage bottlenecks when dealing with billions of potential host galaxies, which is unsustainable for future large-scale surveys. To address this, we propose LenNet, an object detection model that directly identifies lenses in large, original survey images, eliminating the inefficient cropping step. LenNet is first trained on simulated data to learn gravitational lens features. Then, transfer learning is used to fine-tune it on a limited set of real, labeled samples from the Kilo-Degree Survey (KiDS). Experiments show LenNet performs exceptionally well on real survey data, validating its ability as an efficient and scalable solution for lens discovery in massive astronomical surveys. LenNet’s success in direct lens detection in large images resolves the computational and storage issues of traditional methods. The strategy of using simulated data for initial training and transfer learning with real KiDS data is effective, especially given limited real labeled data. Looking forward, LenNet can enable more efficient lens discovery in future large-scale surveys, accelerating research on dark matter and cosmic expansion.