Deep learning the astrometric signature of dark matter substructure
Deep learning the astrometric signature of dark matter substructure
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DOI：
10.1103/physrevd.104.123541
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发表时间：
2020-08
影响因子：
5
通讯作者：
Kyriakos Vattis;M. Toomey;S. Koushiappas
中科院分区：
文献类型：
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作者：
Kyriakos Vattis;M. Toomey;S. Koushiappas
我们研究了机器学习技术在暗物质子结构天体测量特征检测中的应用。在这个原理的证明中，银河系中的暗物质暗晕群将充当星系外起源源（如类星体）的透镜。我们训练ResNet-18，这是一种最先进的卷积神经网络，可以将类星体群体的角速度图分类为透镜类和非透镜类。我们表明，一个类似SKA的调查与扩展的业务基线可以用来探测银河系的子结构内容。
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 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.