论文标题
质量非特定监督标签（必须）用于增强喷气机
Mass Unspecific Supervised Tagging (MUST) for boosted jets
论文作者
论文摘要
JET识别工具对于在LHC和将来的山着人的新物理搜索至关重要。我们介绍了质量非特异性监督标签（必须）的概念，该标记（必须）依赖于考虑到多变量工具的输入变量，以及宽范围内的横向动量和横向动量变化，以及Jet Uspromenture可观察到的。这种方法不仅为射流质量和横向动量的任意范围提供了一个有效的标签仪，而且还为当前标签者固有的质量相关问题提供了最佳解决方案。通过训练神经网络，我们构建了必须启发的通用和多沟通的喷气标签器，当使用各种新物理信号进行测试时，显然优于实验通常使用的变量，以区分背景信号。这些标签者还可以有效地发现未经培训的信号。也可以建造标签者，以高度信心确定喷气机的插脚，如果发现新的物理信号，这将非常重要。
Jet identification tools are crucial for new physics searches at the LHC and at future colliders. We introduce the concept of Mass Unspecific Supervised Tagging (MUST) which relies on considering both jet mass and transverse momentum varying over wide ranges as input variables - together with jet substructure observables - of a multivariate tool. This approach not only provides a single efficient tagger for arbitrary ranges of jet mass and transverse momentum, but also an optimal solution for the mass correlation problem inherent to current taggers. By training neural networks, we build MUST-inspired generic and multi-pronged jet taggers which, when tested with various new physics signals, clearly outperform the variables commonly used by experiments to discriminate signal from background. These taggers are also efficient to spot signals for which they have not been trained. Taggers can also be built to determine, with a high degree of confidence, the prongness of a jet, which would be of utmost importance in case a new physics signal is discovered.
