Speaker
Description
Initial studies have suggested generative adversarial networks (GANs) have promise as fast simulations within HEP. These studies, while promising, have been insufficiently precise and also, like GANs in general, suffer from stability issues. We apply GANs to to generate full particle physics events (not individual physics objects), and to large weak lensing cosmology convergence maps. We explore recent developments in convergence, such as ‘optimal transport’ GANs; explore representations that match the structure of the detector better than images; and evaluate the precision and generalisability of the generated datasets.
In addition we demonstrate a framework making use of distributed computing on the Cori supercomputer at NERSC launched via interactive jupyter notebook sessions,. This allows for tackling high-resolution detector data; model selection and hyper-parameter tuning in a productive yet scalable deep learning environment.
