论文标题
从图像分割的强镜图像中提取subhalo质量函数
Extracting the Subhalo Mass Function from Strong Lens Images with Image Segmentation
论文作者
论文摘要
在强镜图像中检测子结构是一条有前途的途径，可以阐明暗物质的性质。但是，这是一项具有挑战性的任务，传统上需要详细的镜头建模和源重建，需要数周的时间来分析每个系统。我们使用机器学习来规避对镜头和源建模的需求，并开发神经网络以在图像中定位Subhalos，并使用图像分割的技术确定其质量。该网络在图像上进行了训练，该图像具有一个位于爱因斯坦环附近的单个subhalo，跨越了广泛的明显源幅度。然后，网络能够用质量$ m \ gtrsim 10^{8.5} m _ {\ odot} $解决Subhalos。以这种方式训练可以使网络学习光的重力镜头，并且值得注意的是，即使是在远离爱因斯坦环的位置，它也能够检测到整个子结构的种群，而不是训练中使用的。在广泛的明显源幅度上，假阳性速率约为每100张图像的三个假subhalos，主要来自该信噪比最轻的可检测到的Subhalo。具有良好的准确性和低阳性速率，计算分配给每个Subhalo类的像素的数量，而不是多个图像，可以测量Subhalo质量函数（SMF）。当超过三个质量垃圾箱从$ 10^9M _ {\ odot} $ - $ 10^{10} m _ {\ odot} $测量时，SMF斜率被恢复为50张图像的36％，而使用Hubble Space Space telescope类似于类似于Hubble Space的1000张图像，这将提高到10％。
Detecting substructure within strongly lensed images is a promising route to shed light on the nature of dark matter. However, it is a challenging task, which traditionally requires detailed lens modeling and source reconstruction, taking weeks to analyze each system. We use machine-learning to circumvent the need for lens and source modeling and develop a neural network to both locate subhalos in an image as well as determine their mass using the technique of image segmentation. The network is trained on images with a single subhalo located near the Einstein ring across a wide range of apparent source magnitudes. The network is then able to resolve subhalos with masses $m\gtrsim 10^{8.5} M_{\odot}$. Training in this way allows the network to learn the gravitational lensing of light, and remarkably, it is then able to detect entire populations of substructure, even for locations further away from the Einstein ring than those used in training. Over a wide range of the apparent source magnitude, the false-positive rate is around three false subhalos per 100 images, coming mostly from the lightest detectable subhalo for that signal-to-noise ratio. With good accuracy and a low false-positive rate, counting the number of pixels assigned to each subhalo class over multiple images allows for a measurement of the subhalo mass function (SMF). When measured over three mass bins from $10^9M_{\odot}$--$10^{10} M_{\odot}$ the SMF slope is recovered with an error of 36% for 50 images, and this improves to 10% for 1000 images with Hubble Space Telescope-like noise.
