  Higgsino in supersymmetric standard models can play the role of dark matter
particle. In conjunction with the naturalness criterion, the higgsino mass
parameter is expected to be around the electroweak scale. In this work, we
explore the potential of probing the nearly degenerate light higgsinos with
machine learning at the LHC. By analyzing jet images and other jet substructure
information, we use the Convolutional Neural Network(CNN) to enhance the signal
significance. We find that our deep learning jet image method can improve the
previous result based on the conventional cut-flow by about a factor of two at
the High-Luminosity LHC.
