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

Nov 17, 2022, 5:00 PM
20m
Remote

Remote

Speaker

Ahmed Hammad (Seoultech)

Description

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 de-correlating 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.

Presentation materials