LenNet: Direct Detection and Localization of Strong Gravitational Lenses in Wide-Field Sky Survey Images

璞凡 刘¹Hui Li¹Ziqi Li¹Rui Li^1*Xiaoyue Cao¹Hao Su²Ran Li³Nicola R. Napolitano²V E Koopmans⁴Valerio Busillo⁵Crescenzo Tortora⁵Liang Gao¹

• ¹Zhengzhou University, Zhengzhou, China
• ²Universita degli Studi di Napoli Federico II Dipartimento di Fisica Ettore Pancini, Naples, Italy
• ³Beijing Normal University School of Physics and Astronomy, Beijing, China
• ⁴Rijksuniversiteit Groningen, Groningen, Netherlands
• ⁵Osservatorio Astronomico di Capodimonte, Naples, Italy

Strong gravitational lenses are invaluable tools for addressing fundamental questions in astrophysics, from the nature of dark matter to the expansion of the universe. While current sky surveys have successfully identified thousands of lens candidates, the search methods employed face a critical challenge. The conventional approach relies on a "crop-and-classify" strategy, where small images are first cut out around billions of potential host galaxies before being individually classified. This process creates a significant computational and storage bottleneck that is unsustainable for future large-scale surveys. To overcome this limitation, we propose LenNet, an object detection model that identifies lenses directly within large, original survey images. Our method completely bypasses the inefficient cropping step by framing the problem as a direct detection and localization task. We initially train LenNet on simulated data to learn the complex features of gravitational lenses and then use transfer learning to fine-tune the model on a limited set of real, labeled examples from the Kilo-Degree Survey (KiDS). Our experiments show that LenNet performs remarkably well on real survey data, validating its potential as a highly efficient and scalable solution for lens discovery in massive astronomical surveys.