Detector Simulation

Detector simulation in high-energy physics is crucial for analyzing particle collisions but is computationally expensive. Current research focuses on developing fast, accurate surrogate models using deep generative models like GANs, VAEs, normalizing flows, and transformers, often incorporating techniques like self-supervised learning and relational reasoning to capture complex detector responses and intra-event correlations. These advancements aim to significantly reduce the computational burden of simulating large datasets from complex detectors at facilities like the LHC and future colliders, enabling more efficient data analysis and potentially accelerating scientific discovery.