Conveners
Generative Models and Simulation
- Ramon Winterhalder (UCLouvain)
The full simulation of particle colliders incurs a significant computational cost. Among the most resource-intensive steps are detector simulations. It is expected that future developments, such as higher collider luminosities and highly granular calorimeters, will increase the computational resource requirement for simulation beyond availability. One possible solution is generative neural...
The use of machine learning for collider data generation has become a significant area of study within particle physics. This interest arises from the increasing computational difficulties associated with traditional Monte Carlo simulation methods, especially in the context of future high-luminosity colliders. Representing collider data as particle clouds introduces several advantageous...
Simulation of calorimeter response is a crucial part of detector study for modern high energy. The computational cost of conventional MC-based simulation becoming a major bottleneck with the increasingly large and high granularity design. We propose a 2-step generative model for fast calorimeter simulation based on Vector-Quantized Variational Autoencoder (VQ-VAE). This model achieves a fast...
