A conditional latent autoregressive recurrent model for generation and
  forecasting of beam dynamics in particle accelerators
Mahindra Rautela Alan Williams Alexander Scheinker
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 into a lower-dimensional latent distribution and a Long Short-Term Memory
(LSTM) network capturing temporal dynamics in an autoregressive manner. The
CLARM can generate projections at various accelerator modules by sampling and
decoding the latent space representation. The model also forecasts future
states (downstream locations) of charged particles from past states (upstream
locations). The results demonstrate that the generative and forecasting ability
of the proposed approach is promising when tested against a variety of
evaluation metrics.