Calorimeter Shower

Calorimeter shower simulation is a computationally intensive process crucial for analyzing particle collisions at high-energy physics experiments like the Large Hadron Collider. Current research focuses on developing fast, accurate surrogate models using machine learning, employing architectures like variational autoencoders, normalizing flows, diffusion models, and generative adversarial networks to generate realistic calorimeter shower data orders of magnitude faster than traditional methods. These advancements significantly reduce the computational bottleneck in data analysis, enabling more efficient processing of the massive datasets generated by modern particle accelerators and improving the precision of physics analyses. The development of geometry-independent models further enhances the applicability and scalability of these techniques.