论文标题
深入学习暗物质子结构的天文标志
Deep learning the astrometric signature of dark matter substructure
论文作者
论文摘要
我们研究机器学习技术在检测暗物质子结构的天体特征中的应用。在这一原则的证明中，以银河系中的暗物质苏巴洛斯人的人口将充当诸如类星体之类的诸如类星体之类起源的镜头。我们训练{\ it Resnet-18}，这是一种最先进的卷积神经网络，可将类星体种群的角速度图分类为镜头且无镜头类。我们表明，具有扩展操作基线的类似SKA的调查可用于探测银河系的子结构内容，并演示如何将公理归因用于在镜头图中定位子结构。
We study the application of machine learning techniques for the detection of the astrometric signature of dark matter substructure. In this proof of principle a population of dark matter subhalos in the Milky Way will act as lenses for sources of extragalactic origin such as quasars. We train {\it ResNet-18}, a state-of-the-art convolutional neural network to classify angular velocity maps of a population of quasars into lensed and no lensed classes. We show that an SKA -like survey with extended operational baseline can be used to probe the substructure content of the Milky Way, and demonstrate how axiomatic attribution can be used to localize substructures in lensing maps.
