  Based on the jet image approach, which treats the energy deposition in each
calorimeter cell as the pixel intensity, the Convolutional neural network (CNN)
method has been found to achieve a sizable improvement in jet tagging compared
to the traditional jet substructure analysis. In this work, the Mask R-CNN
framework is adopted to reconstruct Higgs jets in collider-like events, with
the effects of pileup contamination taken into account. This automatic jet
reconstruction method achieves higher efficiency of Higgs jet detection and
higher accuracy of Higgs boson four-momentum reconstruction than traditional
jet clustering and jet substructure tagging methods. 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. The outputs of the
network also serve as new handles for the $t\bar{t}$ background suppression,
complementing to traditional jet substructure variables.
