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

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