Speaker
Description
The CMS Generative Machine Learning Group, at the DESY round table, will showcase three distinct projects, each utilizing point cloud-based generative models to advance particle physics research. The first project, "Attention to Mean Fields for Particle Cloud Generation," features an attention-based generative model that adeptly processes complex collider data represented as point clouds, demonstrating effectiveness on the JetNet150 and CaloChallenge datasets. The second project, "DeepTreeGAN," explores novel techniques for iterative up- and downscaling of point clouds, inspired by the tree-based development of particle showers. Finally, CaloPointFlow presents a generative model using normalizing flows, specifically tailored for efficient and high-fidelity generation of calorimeter showers as point clouds, providing a speedier and more efficient alternative to conventional simulations. Together, these projects underscore the transformative role of point cloud-based generative models in the realm of particle physics.
