Hybrid Algorithms for Enhanced Vertex and Track Reconstruction

In the realm of particle physics, the Belle II experiment seeks to uncover the mysteries of subatomic particles and the origins of the universe through the study of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{B}$</tex> mesons. Central to Belle II is the vertex detector system, crucial for reconstructing particle trajectories. This paper delves into the development of innovative hybrid algorithms for particle vertex and track reconstruction, leveraging mathematical modeling and machine learning techniques. By integrating these methods, the study aims to enhance the precision and efficiency of trajectory reconstruction. The proposed hybrid algorithms involve initial vertex estimation with iterative refinement and difference array classification with noise removal in track reconstruction. Through a comprehensive evaluation process, the algorithm demonstrates improved accuracy and robustness against noise and overfitting issues, positioning itself as a promising advancement in the field of particle physics research. The source code of methods proposed in this paper is provided at: https://github.com/BeyondWillJ/ML-4.