论文标题
DeElema：缺少信息搜索，深入学习进行质量估计
DeeLeMa: Missing information search with Deep Learning for Mass estimation
论文作者
论文摘要
我们介绍了DeElema，这是一个基于深度学习的网络，用于分析高能粒子碰撞中的能量和动量。这种新颖的方法是专门设计的，以应对分析与多个不可见粒子发生碰撞事件的挑战，这些粒子在许多高能物理实验中很普遍。 DeElema是根据事件拓扑的运动学约束和对称性构建的。我们表明，即使在存在组合不确定性和检测器污迹效应的情况下，DeElema也可以稳健地估计质量分布。该方法是灵活的，可以通过利用相关的运动对称性来应用于各种事件拓扑。这项工作为分析高能粒子碰撞数据打开了激动人心的机会，我们认为Deelema有可能成为高能物理学界的宝贵工具。
We introduce DeeLeMa, a deep learning-based network for the analysis of energy and momentum in high-energy particle collisions. This novel approach is specifically designed to address the challenge of analyzing collision events with multiple invisible particles, which are prevalent in many high-energy physics experiments. DeeLeMa is constructed based on the kinematic constraints and symmetry of the event topologies. We show that DeeLeMa can robustly estimate mass distribution even in the presence of combinatorial uncertainties and detector smearing effects. The approach is flexible and can be applied to various event topologies by leveraging the relevant kinematic symmetries. This work opens up exciting opportunities for the analysis of high-energy particle collision data, and we believe that DeeLeMa has the potential to become a valuable tool for the high-energy physics community.
