Riemannian Data preprocessing in Machine Learning to focus on QCD color structure

TL;DR

This paper introduces a novel Riemannian data preprocessing technique that enhances QCD color structure analysis in particle physics by utilizing the full phase space, improving statistical stability and complementing existing methods.

Contribution

The paper proposes a new Riemann sphere-based preprocessing method that decorrelates QCD structure from kinematics, enabling full phase space analysis and increased data stability in LHC experiments.

Findings

Enlarges the effective data set for QCD analysis at the LHC

Improves statistical stability in QCD structure identification

Complementary to boosted jet analyses for wider phase space coverage

Abstract

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.