标题： HOLISMOKES XV 结合地面和空间成像的强引力透镜搜索 显示英文标题

标题： HOLISMOKES XV. Search for strong gravitational lenses combining ground-based and space-based imaging

摘要： 在过去，研究人员主要依赖于来自单一望远镜的单分辨率图像来检测引力透镜。 我们提出了一种寻找星系尺度透镜的方法，这是首次将高分辨率单波段图像（在我们的案例中是哈勃空间望远镜，HST）与低分辨率多波段图像（在我们的案例中是遗产调查，LS）结合使用机器学习。 这种方法旨在模拟未来任务（如结合欧几里得和鲁宾天文台的太空和时间遗产调查（LSST））将采用的操作策略。 为了弥补用于网络训练的透镜星系图像的稀缺性，我们通过将弧形特征叠加到HST图像上生成了模拟透镜，保存了透镜参数，并在LS图像中复制了透镜系统。 我们测试了四种基于ResNet-18的架构：（1）使用单波段HST图像，（2）使用LS的三个波段图像，（3）在将LS图像插值到HST像素尺度后叠加这些图像以进行同时处理，（4）在全连接层之前将HST的ResNet分支与LS的ResNet分支合并。 我们通过为每个模型创建接收者操作特征（ROC）曲线并比较它们的输出分数来比较这些架构的性能。 在假阳性率为$10^{-4}$的情况下，真阳性率分别为HST的$\sim$0.41，LS的$\sim$0.45，叠加图像的$\sim$0.51以及合并分支的$\sim$0.55。 我们的结果表明，与依赖单一仪器数据的模型相比，整合HST和LS图像的模型显著提高了星系尺度透镜的检测能力。 这些结果展示了同时使用欧几里得和LSST图像的潜在好处，因为广域成像调查预计将发现大约100,000个透镜。 显示英文摘要

摘要： In the past, researchers have mostly relied on single-resolution images from individual telescopes to detect gravitational lenses. We propose a search for galaxy-scale lenses that, for the first time, combines high-resolution single-band images (in our case the Hubble Space Telescope, HST) with lower-resolution multi-band images (in our case Legacy survey, LS) using machine learning. This methodology aims to simulate the operational strategies that will be employed by future missions, such as combining the images of Euclid and the Rubin Observatory's Legacy Survey of Space and Time (LSST). To compensate for the scarcity of lensed galaxy images for network training, we have generated mock lenses by superimposing arc features onto HST images, saved the lens parameters, and replicated the lens system in the LS images. We test four architectures based on ResNet-18: (1) using single-band HST images, (2) using three bands of LS images, (3) stacking these images after interpolating the LS images to HST pixel scale for simultaneous processing, and (4) merging a ResNet branch of HST with a ResNet branch of LS before the fully connected layer. We compare these architecture performances by creating Receiver Operating Characteristic (ROC) curves for each model and comparing their output scores. At a false-positive rate of $10^{-4}$, the true-positive rate is $\sim$0.41, $\sim$0.45, $\sim$0.51 and $\sim$0.55, for HST, LS, stacked images and merged branches, respectively. Our results demonstrate that models integrating images from both the HST and LS significantly enhance the detection of galaxy-scale lenses compared to models relying on data from a single instrument. These results show the potential benefits of using both Euclid and LSST images, as wide-field imaging surveys are expected to discover approximately 100,000 lenses.