Boosted top quark tagging and polarization measurement using machine learning

Abstract

Machine learning techniques are used for treating jets as images to explore the performance of boosted top quark tagging. Tagging performances are studied in both hadronic and leptonic channels of top quark decay, employing a convolutional neural network (CNN) based technique along with boosted decision trees (BDT). This computer vision approach is also applied to distinguish between left and right polarized top quarks. In this context, an experimentally measurable asymmetry variable is proposed to estimate the polarization. Results indicate that the CNN based classifier is more sensitive to top quark polarization than the standard kinematic variables. It is observed that the overall tagging performance in the leptonic channel is better than the hadronic case, and the former also serves as a better probe for studying polarization.

References in corpus (7)

• 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
• Jet substructure as a new Higgs search channel at the LHC
• Jet substructure as a new Higgs search channel at the LHC
• Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
• Lepton distribution as a probe of new physics in production and decay of the t quark and its polarization
• Polarized tops from new physics: signals and observables

Cited by in corpus (8)

• Machine Learning in High Energy Physics: A review of heavy-flavor jet tagging at the LHC
• Precise probing and discrimination of third-generation scalar leptoquarks
• Azimuthal Angular Correlation as a Boosted Top Jet Substructure
• Pulse Shape Simulation and Discrimination using Machine-Learning Techniques
• Boosted top tagging and its interpretation using Shapley values
• Probing sub-TeV Higgsinos aided by a ML-based top tagger in the context of Trilinear RPV SUSY
• Tagging a Boosted Top quark with a $\tau$ final state
• Tagging ultra-boosted jets at FCC-hh using machine learning techniques