dN/dx Reconstruction with Deep Learning for High-Granularity TPCs
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908938086121472 |
|---|---|
| author | Zhao, Guang Chang, Yue Zhang, Jinxian Wu, Linghui Qi, Huirong She, Xin Dong, Mingyi Sun, Shengsen Wang, Jianchun Wang, Yifang Yu, Chunxu |
| author_facet | Zhao, Guang Chang, Yue Zhang, Jinxian Wu, Linghui Qi, Huirong She, Xin Dong, Mingyi Sun, Shengsen Wang, Jianchun Wang, Yifang Yu, Chunxu |
| contents | Particle identification (PID) is essential for future particle physics experiments such as the Circular Electron-Positron Collider and the Future Circular Collider. A high-granularity Time Projection Chamber (TPC) not only provides precise tracking but also enables dN/dx measurements for PID. The dN/dx method estimates the number of primary ionization electrons, offering significant improvements in PID performance. However, accurate reconstruction remains a major challenge for this approach. In this paper, we introduce a deep learning model, the Graph Point Transformer (GraphPT), for dN/dx reconstruction. In our approach, TPC data are represented as point clouds. The network backbone adopts a U-Net architecture built upon graph neural networks, incorporating an attention mechanism for node aggregation specifically optimized for point cloud processing. The proposed GraphPT model surpasses the traditional truncated mean method in PID performance. In particular, the $K/π$ separation power improves by approximately 10% to 20% in the momentum interval from 5 to 20 GeV/c. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_10628 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | dN/dx Reconstruction with Deep Learning for High-Granularity TPCs Zhao, Guang Chang, Yue Zhang, Jinxian Wu, Linghui Qi, Huirong She, Xin Dong, Mingyi Sun, Shengsen Wang, Jianchun Wang, Yifang Yu, Chunxu High Energy Physics - Experiment Particle identification (PID) is essential for future particle physics experiments such as the Circular Electron-Positron Collider and the Future Circular Collider. A high-granularity Time Projection Chamber (TPC) not only provides precise tracking but also enables dN/dx measurements for PID. The dN/dx method estimates the number of primary ionization electrons, offering significant improvements in PID performance. However, accurate reconstruction remains a major challenge for this approach. In this paper, we introduce a deep learning model, the Graph Point Transformer (GraphPT), for dN/dx reconstruction. In our approach, TPC data are represented as point clouds. The network backbone adopts a U-Net architecture built upon graph neural networks, incorporating an attention mechanism for node aggregation specifically optimized for point cloud processing. The proposed GraphPT model surpasses the traditional truncated mean method in PID performance. In particular, the $K/π$ separation power improves by approximately 10% to 20% in the momentum interval from 5 to 20 GeV/c. |
| title | dN/dx Reconstruction with Deep Learning for High-Granularity TPCs |
| topic | High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2510.10628 |