论文标题
暗物质Subhalos，强大的镜头和机器学习
Dark Matter Subhalos, Strong Lensing and Machine Learning
论文作者
论文摘要
我们研究了将机器学习技术应用于强镜星系图像的可能性，以检测透镜系统中暗物质sub-Halos频谱中的低质量切割。我们生成包含七个不同类别子结构的系统的镜头图像，对应于较低的质量截止，范围从$ 10^9m_ \ odot $下降到$ 10^6m_ \ odot $。我们使用卷积神经网络对这些图像进行多分类排序，并看到该算法能够在数量级内正确识别较低的质量截止，以优于93％的精度。
We investigate the possibility of applying machine learning techniques to images of strongly lensed galaxies to detect a low mass cut-off in the spectrum of dark matter sub-halos within the lens system. We generate lensed images of systems containing substructure in seven different categories corresponding to lower mass cut-offs ranging from $10^9M_\odot$ down to $10^6M_\odot$. We use convolutional neural networks to perform a multi-classification sorting of these images and see that the algorithm is able to correctly identify the lower mass cut-off within an order of magnitude to better than 93% accuracy.
