Reconstruction of Particle Flow Energy Distribution Using Deep Learning Algorithms

• URL: http://arxiv.org/abs/2410.07250v1
• Date: Tue, 8 Oct 2024 11:49:18 GMT
• Title: Reconstruction of Particle Flow Energy Distribution Using Deep Learning Algorithms
• Authors: Han Zhang, Shengxiang Lin, Xingyi Zhang, Yu Wang, Yangguang Zhang,
• Abstract summary: Recent advancements involve using deep learning to process calorimeter images from various sub-detectors 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. Test dataset of jet events is utilized to analyze and compare models' performance in handling anomalous high-energy events.
• Score: 8.5980103509356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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