LenNet：广域巡天图像中强引力透镜的直接检测与定位
LenNet: Direct Detection and Localization of Strong Gravitational Lenses in Wide-Field Sky Survey Images
AI总结：
针对传统裁剪-分类方法在强引力透镜搜索中的计算瓶颈，本文提出LenNet目标检测模型，直接在原始巡天图像中检测定位透镜，结合模拟预训练与KiDS真实数据微调，实验验证其高效可扩展性。
AI中文摘要：
强引力透镜是解决天体物理学中基本问题的宝贵工具，从暗物质的本质到宇宙的膨胀。虽然当前的巡天项目已成功识别出数千个透镜候选体，但所采用的搜索方法面临一个关键挑战。传统方法依赖于“裁剪-分类”策略，即首先在数十亿个潜在宿主星系周围裁剪出小图像，然后逐一分类。这一过程造成了显著的计算和存储瓶颈，对于未来大规模巡天项目而言不可持续。为克服这一限制，我们提出了LenNet，一种目标检测模型，可直接在大型原始巡天图像中识别透镜。我们的方法完全绕过了低效的裁剪步骤，将问题构建为直接检测与定位任务。我们首先在模拟数据上训练LenNet，以学习引力透镜的复杂特征，然后利用迁移学习，在来自千度巡天（KiDS）的有限真实标注样本上对模型进行微调。实验表明，LenNet在真实巡天数据上表现优异，验证了其作为大规模天文巡天中透镜发现的高效且可扩展解决方案的潜力。
英文摘要：
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.
