Automatic detection of boosted Higgs boson and top quark jets in an event image

Abstract

We build a deep neural network based on the Mask R-CNN framework to detect the Higgs jets and top quark jets in any event image. We propose an algorithm to assign the top quark final states at the ground truth level so that the network can be trained in a supervised manner. A new jet branch is added to the network, which uses constituent information to predict the four-momenta of the original parton, thus intrinsically implementing the pileup mitigation. The network can predict both the shapes and the momenta of target jets. We show that the network surpasses the LorentzNet in top and Higgs tagging and the PELICAN network in momentum regression for certain cases, in terms of reconstruction efficiency and accuracy. We also show that the performance of the network does not degrade much when applied to events of a process different from the trained one and to events with overlapping jets.

20 pages, 12 figures, version accepted for publication in PRD

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Cited by in corpus (5)

• Jet Discrimination with Quantum Complete Graph Neural Network
• Boosted top tagging and its interpretation using Shapley values
• Interplay of Traditional Methods and Machine Learning Algorithms for Tagging Boosted Objects
• Jet Reconstruction with Mamba Networks in Collider Events
• Tagging ultra-boosted jets at FCC-hh using machine learning techniques