Reconstructing boosted Higgs jets from event image segmentation
Jinmian Li, Tianjun Li, Fang-Zhou Xu
The Mask R-CNN framework is adopted to reconstruct Higgs jets in Higgs boson production events, with the effects of pileup contamination taken into account. This automatic reconstruction method achieves higher efficiency of Higgs jet detection and higher accuracy of Higgs boson four-momentum reconstruction than traditional jet clustering and tagging methods using substructure information. Moreover, the Mask R-CNN trained on events containing a single Higgs jet is capable of detecting one or more Higgs jets in events of several different processes, without apparent degradation in reconstruction efficiency and accuracy. Taking the outputs of the network as new features to complement traditional jet substructure variables, the signal events can be discriminated from background events even further.