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.
翻译:在高透视图像中检测子结构是一个很有希望的途径,可以揭示暗物质的性质。然而,这是一个具有挑战性的任务,传统上需要详细的透镜模型和源的重建,每个系统都要花几周时间分析。我们使用机器学习来规避镜头和源模型的需要,并开发一个神经网络,将子卤素定位在图像中,并使用图像分割技术来确定其质量。这个网络用位于爱因斯坦环附近的一个子卤素图像进行培训,该子卤素分布范围很广,源量范围很广。然后网络能够用质量为$m\gtrsim 10 的图像解决亚卤素问题,通常来自可探测的亚焦素 $$mgtrsimm 10 10} M ⁇ odo 。 通过这种培训,网络可以学习光学透镜和源的光学透镜的光学透光学透镜透镜,然后可以探测整个子结构组群,即使距离爱爱因子圈更远的地方比训练的更远的地方。在一系列的源级中,假正值为每100个图像大约三个假的亚焦层,主要来自可探测的亚焦值 $__%_%_8=9的图像,用于该信号的测量的Slxxx每个测量的精确度,每个的测算数。