Speaker
Description
With current and future high-energy collider experiments' vast data collecting capabilities comes an increasing demand for computationally efficient simulations. Generative machine learning models allow fast event generation, yet so far are largely constrained to fixed data and detector geometries.
We introduce a Deep Sets based permutation equivariant generative adversarial network (GAN) for generation of permutation invariant point clouds with variable cardinality - a flexible data structure optimal for collider events such as jets. The generator utilizes an interpretable global latent vector and does not rely on pairwise information sharing between particles, leading to a significant speed-up over graph-based approaches. The model can be fine-tuned for minimal information sharing between particles and model complexity. We show that our GAN scales well to large particle multiplicities and achieves high generation fidelity for quark jets.
