Deep Learning Jet Image as a Probe of Light Higgsino Dark Matter at the LHC

TL;DR

This paper demonstrates that deep learning techniques applied to jet images significantly improve the detection sensitivity of nearly degenerate light higgsinos, potential dark matter candidates, at the LHC.

Contribution

The study introduces a CNN-based jet image analysis method to enhance higgsino detection, outperforming traditional cut-flow approaches at the High-Luminosity LHC.

Findings

Deep learning jet image method doubles the signal significance.

Improved detection sensitivity for light higgsinos at the LHC.

Potential to better probe dark matter candidates in supersymmetry.

Abstract

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.