论文标题
变压器作为强透镜检测器 - 从模拟到调查
Transformers as Strong Lens Detectors- From Simulation to Surveys
论文作者
论文摘要
随着即将进行的大规模调查，例如LSST，我们希望在许多数量级的数据中发现约10^5 $强的重力透镜。在这种情况下，非自动化技术的用法太耗时了，因此对于科学而言是不切实际的。因此，机器学习技术开始成为以前方法的替代方法。在我们以前的工作中，我们根据自我注意的原则提出了一种新的机器学习体系结构，受过训练，可以在博洛尼亚镜头挑战中找到强烈的重力镜头。与当前的最新CNN模型相比，与更简单的CNN和高度竞争性能相比，基于自我注意力的模型具有明显的优势。我们将提出的模型应用于Kilo学位调查，以确定一些新的强晶状体候选者。但是，这些模型的应用并不那么有利。因此，在整个本文中，我们提出了这种方法的陷阱，并提出了可能的解决方案，例如转移学习。
With the upcoming large-scale surveys like LSST, we expect to find approximately $10^5$ strong gravitational lenses among data of many orders of magnitude larger. In this scenario, the usage of non-automated techniques is too time-consuming and hence impractical for science. For this reason, machine learning techniques started becoming an alternative to previous methods. In our previous work, we proposed a new machine learning architecture based on the principle of self-attention, trained to find strong gravitational lenses on simulated data from the Bologna Lens Challenge. Self-attention-based models have clear advantages compared to simpler CNNs and highly competing performance in comparison to the current state-of-art CNN models. We apply the proposed model to the Kilo Degree Survey, identifying some new strong lens candidates. However, these have been identified among a plethora of false positives, which made the application of this model not so advantageous. Therefore, throughout this paper, we investigate the pitfalls of this approach, and possible solutions, such as transfer learning, are proposed.
