论文标题
Riemannian数据在机器学习中的预处理专注于QCD颜色结构
Riemannian Data preprocessing in Machine Learning to focus on QCD color structure
论文作者
论文摘要
识别过程的量子染色体动力学（QCD）颜色结构提供了其他信息，以增强大型强子对撞机（LHC）的新物理搜索的范围。随着信息在有限的相空间中良好的位置，在增强粒子的衰减过程中对QCD颜色结构的分析已被发现。尽管这种增强的喷射分析提供了一种识别颜色结构的有效方法，但受约束的相空间减少了可用数据的数量，从而降低了显着性。在这封信中，我们使用Riemann Sphere提供了一种简单但新颖的数据预处理方法，以通过将QCD结构从运动学学上解矛来利用完整的空间。我们可以通过有效地关注QCD结构的可测试数据集的大小来实现统计稳定性。我们在LHC运行2的有限统计数据中证明了我们方法的功能。我们的方法与常规的增强喷气式分析互补，用于利用QCD信息在广泛的相空间范围内。
Identifying the quantum chromodynamics (QCD) color structure of processes provides additional information to enhance the reach for new physics searches at the Large Hadron Collider (LHC). Analyses of QCD color structure in the decay process of a boosted particle have been spotted as information becomes well localized in the limited phase space. While these kind of a boosted jet analyses provide an efficient way to identify a color structure, the constrained phase space reduces the number of available data, resulting in a low significance. In this letter, we provide a simple but a novel data preprocessing method using a Riemann sphere to utilize a full phase space by decorrelating QCD structure from a kinematics. We can achieve a statistical stability by enlarging the size of testable data set with focusing on QCD structure effectively. We demonstrate the power of our method at the finite statistics of the LHC Run 2. Our method is complementary to conventional boosted jet analyses in utilizing QCD information over the wide range of a phase space.
