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

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 transforming six-dimensional phase space into a lower-dimensional latent distribution and a Long Short-Term Memory network capturing temporal dynamics in an autoregressive manner. The CLARM can generate projections at various accelerator modul

SUBMITTER: Rautela M 

PROVIDER: S-EPMC11300895 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature