A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators

arXiv:2403.13858v1 Announce Type: cross
Abstract: Particle accelerators are complex systems that focus, guide, and accelerate intense charged particle beams to high energy. Beam diagnostics present a challenging problem due to limited non-destructive measurements, computationally demanding simulations, and inherent uncertainties in the system. We propose a two-step unsupervised deep learning framework named as Conditional Latent Autoregressive Recurrent Model (CLARM) for learning the spatiotemporal dynamics of charged particles in accelerators. CLARM consists of a Conditional Variational Autoencoder (CVAE) transforming six-dimensional phase space …

abstract accelerators arxiv complex systems cs.cv cs.lg diagnostics dynamics energy focus forecasting guide particle simulations systems type