论文标题
通过神经网络在强力镜头中寻找暗物质Subhalos
Hunting for Dark Matter Subhalos in Strong Gravitational Lensing with Neural Networks
论文作者
论文摘要
暗物质子结构很有趣，因为它们可以揭示暗物质的特性。与我们当地组中观察到的矮星系卫星的种群相比，冷暗物质的无碰撞N体模拟显示出更多的亚结构。因此，了解Subhalos在宇宙学规模的人口和特性将是对冷暗物质的有趣测试。近年来，已经有可能在强烈的延伸背景星系的图像附近检测单个暗物质Subhalos。在这项工作中，我们讨论了使用深层神经网络检测暗物质Subhalos的可能性，并通过模拟数据显示了一些初步结果。我们发现，神经网络不仅在检测多个暗物质Subhalos时显示出令人鼓舞的结果，而且还学会拒绝没有Subhalo的光滑镜头上的镜头上的Subhalos。
Dark matter substructures are interesting since they can reveal the properties of dark matter. Collisionless N-body simulations of cold dark matter show more substructures compared with the population of dwarf galaxy satellites observed in our local group. Therefore, understanding the population and property of subhalos at cosmological scale would be an interesting test for cold dark matter. In recent years, it has become possible to detect individual dark matter subhalos near images of strongly lensed extended background galaxies. In this work, we discuss the possibility of using deep neural networks to detect dark matter subhalos, and showing some preliminary results with simulated data. We found that neural networks not only show promising results on detecting multiple dark matter subhalos, but also learn to reject the subhalos on the lensing arc of a smooth lens where there is no subhalo.
