论文标题
深入学习定向暗物质搜索
Deep learning for Directional Dark Matter search
论文作者
论文摘要
我们提供了一种算法，用于检测NewsDM检测器中记录的可能的暗物质粒子相互作用。 NewsDM（WIMP搜索方向测量的核乳液）是地下直接检测暗物质搜索实验。核乳液中最新发展的使用允许探测WIMP参数空间中的新区域。定向方法是NewsDM实验的关键特征，它为克服“中微子地板”的独特机会。深度神经网络用于潜在的DM信号和各种背景类别之间的分离。在本文中，我们介绍了深3D卷积神经网络的使用，以考虑数据集的物理特征，并报告所需的$ 10^4 $背景拒绝功率的实现。
We provide an algorithm for detection of possible dark matter particle interactions recorded within NEWSdm detector. The NEWSdm (Nuclear Emulsions for WIMP Search directional measure) is an underground Direct detection Dark Matter search experiment. The usage of recent developments in the nuclear emulsions allows probing new regions in the WIMP parameter space. The directional approach, which is the key feature of the NEWSdm experiment, gives the unique chance of overcoming the "neutrino floor". Deep Neural Networks were used for separation between potential DM signal and various classes of background. In this paper, we present the usage of deep 3D Convolutional Neural Networks to take into account the physical peculiarities of the datasets and report the achievement of the required $10^4$ background rejection power.
