Calorimeter Simulation

Calorimeter simulation, crucial for analyzing particle physics experiments, aims to efficiently and accurately model the energy deposition of particles in detectors. Current research heavily utilizes machine learning, focusing on generative models like generative adversarial networks (GANs), variational autoencoders (VAEs), normalizing flows, and score-based models, to drastically speed up simulations compared to traditional Monte Carlo methods. These advancements are vital for handling the massive datasets generated by high-energy physics experiments, enabling faster data analysis and potentially unlocking new discoveries. Furthermore, research explores optimizing model architectures for specific calorimeter geometries and improving the fidelity of simulated data.