Authors

• First authors
  • Kayoung Ban (Yonsei University, South Korea)
  • Dongwoo Kang (KIAS, South Korea)
  • Tae-Geun Kim (Yonsei University, South Korea)
  • Yeji Park (Yonsei University, South Korea)
• Corresponding author
  • Seong Chan Park (Yonsei University, South Korea)

Abstract

We introduce DeeLeMa, a deep learning-based network for the analysis of energy and momentum in high-energy particle collisions. This novel approach is specifically designed to address the challenge of analyzing collision events with multiple invisible particles, which are prevalent in many high-energy physics experiments. DeeLeMa is constructed based on the kinematic constraints and symmetry of the event topologies. We show that DeeLeMa can robustly estimate mass distribution even in the presence of combinatorial uncertainties and detector smearing effects. The approach is flexible and can be applied to various event topologies by leveraging the relevant kinematic symmetries. This work opens up exciting opportunities for the analysis of high-energy particle collision data, and we believe that DeeLeMa has the potential to become a valuable tool for the high-energy physics community.

Important links

• PRR: 5, 043186
• ArXiv: 2212.12836
• GitHub

Citation

@article{Ban:2023mjy,
    author = "Ban, Kayoung and Kang, Dong Woo and Kim, Tae-Geun and Park, Seong Chan and Park, Yeji",
    title = "{Missing information search with deep learning for mass estimation}",
    doi = "10.1103/PhysRevResearch.5.043186",
    journal = "Phys. Rev. Res.",
    volume = "5",
    number = "4",
    pages = "043186",
    year = "2023"
}