Event Building at the Future Circular Collider: Machine Learning Based Track Reconstruction and Particle Flow

Accurate event reconstruction is essential to fully exploit the physics potential of modern particle physics experiments. Particle Flow (PF) algorithms enhance reconstruction efficiency and precision by combining information from multiple subdetectors. In particular, high-quality track information significantly contributes to the overall performance of particle object reconstruction. Future experiments, such as the Future Circular Collider (FCC), are in active development and present new challenges for traditional, detector-specific reconstruction techniques. In response, machine learning-based approaches—notably transformer models—are emerging as powerful alternatives. These architectures offer the flexibility and robustness needed to meet the demands of next-generation particle flow and tracking algorithms.