dN/dx reconstruction with deep learning for high-granularity TPCs

A bstract Particle identification (PID) is essential for future particle physics experiments such as the Circular Electron-Positron Collider (CEPC) and the Future Circular Collider. A high-granularity Time Projection Chamber (TPC) not only provides precise tracking but also enables d N /d x measurements for PID. The d N /d x 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 d N /d x 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, for the CEPC baseline TPC, the K/π separation power improves by approximately 10% to 20% in the momentum interval from 5 to 20 GeV/ c .