Report
Reconstruction of Particle Flow Energy Distribution Using Deep Learning Algorithms
| العنوان: | Reconstruction of Particle Flow Energy Distribution Using Deep Learning Algorithms |
|---|---|
| المؤلفون: | Zhang, Han, Lin, Shengxiang, Zhang, Xingyi, Wang, Yu, Zhang, Yangguang |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Physics (Other) |
| مصطلحات موضوعية: | Physics - Instrumentation and Detectors, Computer Science - Artificial Intelligence |
| الوصف: | 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. Comment: 11 pages, 1 tables, 9 figures Code available at https://github.com/Image-processing-Particle-flow/Project1 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2410.07250 |
| رقم الانضمام: | edsarx.2410.07250 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |