Reconstruction of Particle Flow Energy Distribution Using Deep Learning Algorithms
<|reference_start|>Reconstruction of Particle Flow Energy Distribution Using Deep Learning Algorithms: In high-energy particle physics, extracting information from complex detector signals is crucial for energy reconstruction. Recent advancements involve using deep learning to process calorimeter images from various sub-detectors in experiments like the Large Hadron Collider (LHC) for energy map reconstruction. This paper compares classical algorithms\-MLP, CNN, U-Net, and RNN\-with variants that include self-attention and 3D convolution modules to evaluate their effectiveness in reconstructing the initial energy distribution. Additionally, a test dataset of jet events is utilized to analyze and compare models' performance in handling anomalous high-energy events. The analysis highlights the effectiveness of deep learning techniques for energy image reconstruction and explores their potential in this area.<|reference_end|>
@article{zhang2024reconstruction,
    title={Reconstruction of Particle Flow Energy Distribution Using Deep Learning
  Algorithms},
    author={Han Zhang (1), Shengxiang Lin (2), Xingyi Zhang (3), Yu Wang (4),
  Yangguang Zhang (5) ((1) College of Artificial Intelligence and Automation,
  Hohai University, (2) Faculty of Electronic and Information Engineering,
  Xi'an Jiaotong University, (3) School of Mechanical Engineering, Shanghai
  Jiao Tong University, (4) School of Control and Computer Engineering, North
  China Electric Power University, (5) School of Automation and Electrical
  Engineering, University of Science and Technology Beijing)},
    journal={arXiv preprint arXiv:2410.07250},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.07250},
    primaryClass={physics.ins-det cs.AI}
}