Identification of b-jets using QCD-inspired observables

Abstract

We study the issue of separating hadronic jets that contain bottom quarks ($b$-jets) from jets featuring light partons only. We develop a novel approach to $b$-tagging that exploits the application of QCD-inspired jet substructure observables such as one-dimensional jet angularities and the two-dimensional primary Lund plane. We demonstrate that these observables can be used as inputs to modern machine-learning algorithms to efficiently separate $b$-jets from light ones. In order to test our tagging procedure, we consider simulated events where a $Z$ boson is produced is association with jets and show that using jet angularities as an input for a deep neural network, as well as using images obtained from the primary Lund jet plane as input to a convolutional neural network, one can achieve tagging accuracy comparable with the accuracy of conventional track-based taggers. We argue that the complementary usage of the track-based taggers together with the ones based upon QCD-inspired observables could improve $b$-tagging accuracy.

References in corpus (15)

• Array Programming with NumPy
• An Introduction to PYTHIA 8.2
• Test of lepton universality in beauty-quark decays
• Robust Independent Validation of Experiment and Theory: Rivet version 3
• Gaining (Mutual) Information about Quark/Gluon Discrimination
• Direct observation of the dead-cone effect in QCD
• Dynamical grooming of QCD jets
• Jet Angularities in Z+jet production at the LHC
• Phenomenology of jet angularities at the LHC
• Search for new phenomena with top quark pairs in final states with one lepton, jets, and missing transverse momentum in $pp$ collisions at $\sqrt{s}$= 13 TeV with the ATLAS detector
• Quarks and gluons in the Lund plane
• Study of quark and gluon jet substructure in Z+jet and dijet events from pp collisions
• Higgs boson tagging with the Lund jet plane
• Measurements of the groomed and ungroomed jet angularities in pp collisions at $\sqrt{s}=5.02$ TeV
• Jet grooming through reinforcement learning

Cited by in corpus (7)

• Infrared-safe flavoured anti-$k_T$ jets
• Machine Learning in High Energy Physics: A review of heavy-flavor jet tagging at the LHC
• A consistent resummation of mass and soft logarithms in processes with heavy flavours
• On heavy-flavour jets with Soft Drop
• Dark Sector Showers in the Lund Jet Plane
• Heavy Flavour Jet Substructure
• Jet substructure of light and heavy flavor jets at RHIC