ATLAS Jet Reconstruction, Calibration, and Tagging of Lorentz-boosted Objects

Jet reconstruction in the ATLAS detector takes multiple forms, as motivated by the intended usage of the jet. Different jet definitions are used in particular for the study of QCD jets and jets containing the hadronic decay of boosted massive particles. These different types of jets are calibrated through a series of mostly sequential steps, providing excellent uncertainties, including a first in situ calibration of the jet mass scale. Jet tagging is investigated, including both not-top-quark vs gluon discrimination as well as W/Z boson, H$\to$bb, and top-quark identification. This includes a first look at the use of Boosted Decision Trees and Deep Neural Networks built from jet substructure variables, as well as Convolutional Neural Networks built from jet images. In all cases, these advanced techniques are seen to provide gains over the standard approaches, with the magnitude of the gain depending on the use case. Future methods for improving jet tagging are briefly discussed, including jet substructure-oriented particle flow primarily for W/Z tagging and new subjet reconstruction strategies for H$\to$bb tagging.