论文标题
探索Hadronic Jet分类的普遍性
Exploring the Universality of Hadronic Jet Classification
论文作者
论文摘要
Parton淋浴蒙特卡洛（PSMC）程序之间的射流子结构的建模明显不同。尽管如此，我们观察到在不同PSMC培训的机器学习分类器学习几乎相同的功能。这意味着，当将这些分类器应用于同一PSMC进行测试时，它们会导致几乎相同的性能。该分类器普遍性表明，在一个模拟上训练并在另一个模拟（或数据）上测试的机器学习模型可能是最佳的。我们的观察结果基于对用于模拟Lorentz的浅层神经网络的详细研究，在LHC上提高了Higgs Jet标记。
The modeling of jet substructure significantly differs between Parton Shower Monte Carlo (PSMC) programs. Despite this, we observe that machine learning classifiers trained on different PSMCs learn nearly the same function. This means that when these classifiers are applied to the same PSMC for testing, they result in nearly the same performance. This classifier universality indicates that a machine learning model trained on one simulation and tested on another simulation (or data) will likely be optimal. Our observations are based on detailed studies of shallow and deep neural networks applied to simulated Lorentz boosted Higgs jet tagging at the LHC.
