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

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.

arXiv comment6 pages, 8 figures

References in corpus (41)

• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• An Introduction to PYTHIA 8.2
• FastJet user manual
• Matching matrix elements and shower evolution for top-quark production in hadronic collisions
• Gaining (Mutual) Information about Quark/Gluon Discrimination
• Quark and Gluon Tagging at the LHC
• Seeing in Color: Jet Superstructure
• DeepSphere: Efficient spherical Convolutional Neural Network with HEALPix sampling for cosmological applications
• Graph Neural Networks in Particle Physics
• How to GAN LHC Events
• Large Jets at Hadron Colliders at Order ${\alpha }_s^3$: A Look Inside
• Quark and Gluon Jet Substructure
• Energy flow polynomials: A complete linear basis for jet substructure
• Higgs look-alikes at the LHC
• Finding physics signals with shower deconstruction
• Top Tagging
• The Lund Jet Plane
• Decorrelated Jet Substructure Tagging using Adversarial Neural Networks
• Measurements of $WH$ and $ZH$ production in the $H\to b\bar{b}$ decay channel in $pp$ collisions at 13 TeV with the ATLAS detector
• On the Topic of Jets: Disentangling Quarks and Gluons at Colliders
• Boosting $H\to b\bar{b}$ with Machine Learning
• Casimir Meets Poisson: Improved Quark/Gluon Discrimination with Counting Observables
• DisCo Fever: Robust Networks Through Distance Correlation
• Jet Dipolarity: Top Tagging with Color Flow
• An operational definition of quark and gluon jets
• Quark-Gluon tagging with Shower Deconstruction: Unearthing dark matter and Higgs couplings
• Mapping Machine-Learned Physics into a Human-Readable Space
• Interpretable Deep Learning for Two-Prong Jet Classification with Jet Spectra
• A Theory of Quark vs. Gluon Discrimination
• Pure Samples of Quark and Gluon Jets at the LHC
• Quarks and gluons in the Lund plane
• Portraying Double Higgs at the Large Hadron Collider
• Mass Agnostic Jet Taggers
• Higgs boson tagging with the Lund jet plane
• The DNNLikelihood: enhancing likelihood distribution with Deep Learning
• What's Anomalous in LHC Jets?
• Spectral Analysis of Jet Substructure with Neural Networks: Boosted Higgs Case
• An Optimal Observable for Color Singlet Identification
• Fractal based observables to probe jet substructure of quarks and gluons
• A method for approximating optimal statistical significances with machine-learned likelihoods
• Beyond $M_{t\bar{t}}$: learning to search for a broad $t\bar{t}$ resonance at the LHC