ACAT 2025

Charged Particle Tracking in Drift Chambers Using Reinforcement Learning

Not scheduled

30m

Hamburg, Germany

Poster Track 2: Data Analysis - Algorithms and Tools Poster session with coffee break

Speaker

Yao Zhang

Description

Charged particle tracking for drift chamber is a task in high-energy physics. In this work, we propose using reinforcement learning (RL) to the reconstruction of particle trajectories in drift chambers. By framing the tracking problem as a decision-making process, RL enables the development of more efficient and adaptive tracking algorithms. This approach offering improved performance and flexibility in optimizing end-to-end tracking algorithms for drift chambers.