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.
@article{arxiv.2203.14569,
title = {Deep Learning Jet Image as a Probe of Light Higgsino Dark Matter at the LHC},
author = {Huifang Lv and Daohan Wang and Lei Wu},
journal= {arXiv preprint arXiv:2203.14569},
year = {2022}
}
Comments
24 pages, 9 figures, 1 table, discussions and references added, version accepted by PRD