论文标题
学会识别半可见喷气机
Learning to Identify Semi-Visible Jets
论文作者
论文摘要
我们使用其低级喷气组成部分的模式来训练网络，以识别具有分数暗衰减（半可见喷气机）的喷气机，并通过将其映射到Jet子结构可观察物的空间来探索网络使用的信息的性质。半可见的喷气机来自暗物质颗粒，它们腐烂成暗区（无形）和标准模型（可见）颗粒的混合物。由于喷气机的复杂性质以及黑暗颗粒与喷气轴的动量失衡的对齐方式，这种对象是具有挑战性的，但是这种喷气机尚未受益于专用理论上动机的喷气子结构可观察到的。在JET成分上运行的深网被用作可用信息的探针，并指出当前高级可观察物所捕获的分类功率主要来自低 - $ P_ \ textrm {t} $ jet构成。
We train a network to identify jets with fractional dark decay (semi-visible jets) using the pattern of their low-level jet constituents, and explore the nature of the information used by the network by mapping it to a space of jet substructure observables. Semi-visible jets arise from dark matter particles which decay into a mixture of dark sector (invisible) and Standard Model (visible) particles. Such objects are challenging to identify due to the complex nature of jets and the alignment of the momentum imbalance from the dark particles with the jet axis, but such jets do not yet benefit from the construction of dedicated theoretically-motivated jet substructure observables. A deep network operating on jet constituents is used as a probe of the available information and indicates that classification power not captured by current high-level observables arises primarily from low-$p_\textrm{T}$ jet constituents.
