论文标题
现代夸克 - 格鲁恩标签中的性能与弹性
Performance versus Resilience in Modern Quark-Gluon Tagging
论文作者
论文摘要
从许多方面来说，将类似夸克的喷气式区分开是许多LHC分析的关键挑战。首先，我们在毕田（Pythia）和赫维格（Herwig）模拟中使用已知的差异来表明当最独特的特征与理论不确定性对齐时，脱刀会如何分解。我们建议对插值样品进行条件培训，并结合受控的贝叶斯网络，作为更具弹性的框架。插值参数可用于优化在校准数据集上评估的训练，并测试此优化的稳定性。插值训练在模拟训练网络时也可能有助于跟踪概括错误。
Discriminating quark-like from gluon-like jets is, in many ways, a key challenge for many LHC analyses. First, we use a known difference in Pythia and Herwig simulations to show how decorrelated taggers would break down when the most distinctive feature is aligned with theory uncertainties. We propose conditional training on interpolated samples, combined with a controlled Bayesian network, as a more resilient framework. The interpolation parameter can be used to optimize the training evaluated on a calibration dataset, and to test the stability of this optimization. The interpolated training might also be useful to track generalization errors when training networks on simulation.
