Report Open Access
Barin Pacela, Vitoria
Highly granular calorimeters (HGCAL) will be one of the biggest novelties of the CMS Phase II upgrade and, in general, of the next generation of collider experiments. This kind of detectors offer more opportunities but much more complexity. It has a drawback on the execution time of generic tasks, such particle reconstruction and identification as well as, notably, event simulation. In order to stay within the technical budgets (e.g. computing time) and satisfy the demand for large simulation samples, experiments will have to work on faster and more accurate simulation process. Deep Learning, and in particular generative models, offer an interesting possibility to speed up the simulation technique. Moreover, deep learning solutions are particularly suitable for HGCAL, given the pixelated nature of its active material. This project aims to adapt to HGCAL existing work on GAN for fast simulation.