论文标题
图像分割用于分析星系 - 果实强镜头系统的图像分割
Image segmentation for analyzing galaxy-galaxy strong lensing systems
论文作者
论文摘要
本文的目的是开发一个机器学习模型，以分析主重力镜头并检测强镜星系模拟图像中的黑暗子结构（Subhalos）。使用图像分割的技术，我们将识别Subhalos的任务转变为分类问题，在该问题中，我们将图像中的每个像素标记为来自主镜头，subhalo，subhalo在binned质量范围内，或者都不是。我们的网络仅在爱因斯坦环附近的单个光滑镜头和零或一个subhalo的图像上训练。在具有较大椭圆率，四极杆和章鱼矩的独立测试集中，对于17-25之间的源源幅度，主镜头的面积准确地恢复了。平均而言，仅遗漏了真实面积的1.3％，而真实面积的1.2％被添加到镜头的另一部分。此外，如果Subhalos的光线为$ 10^{8.5} M _ {\ odot} $，则可以检测到它们沿爱因斯坦环的明亮像素中。此外，该模型能够推广到尚未接受过培训的新环境，例如定位具有不同质量或多个大型光滑镜头的多个Subhalos。
The goal of this paper is to develop a machine learning model to analyze the main gravitational lens and detect dark substructure (subhalos) within simulated images of strongly lensed galaxies. Using the technique of image segmentation, we turn the task of identifying subhalos into a classification problem, where we label each pixel in an image as coming from the main lens, a subhalo within a binned mass range, or neither. Our network is only trained on images with a single smooth lens and either zero or one subhalo near the Einstein ring. On an independent test set with lenses with large ellipticities, quadrupole and octopole moments, and for source apparent magnitudes between 17-25, the area of the main lens is recovered accurately. On average, only 1.3% of the true area is missed and 1.2% of the true area is added to another part of the lens. In addition, subhalos as light as $10^{8.5}M_{\odot}$ can be detected if they lie in bright pixels along the Einstein ring. Furthermore, the model is able to generalize to new contexts it has not been trained on, such as locating multiple subhalos with varying masses or more than one large smooth lens.
