Track Reconstruction

Track reconstruction, the process of identifying and connecting individual measurements to form complete tracks of particles or objects, is crucial in diverse fields like high-energy physics and autonomous driving. Current research emphasizes improving efficiency and accuracy using advanced machine learning techniques, particularly graph neural networks (GNNs) and transformer architectures, often combined with classical methods like Kalman filters to leverage the strengths of both approaches. These advancements are driving progress in areas such as particle physics experiments, where they enable analysis of increasingly complex datasets, and autonomous driving, where robust object tracking is essential for safety.