2024
DOI: 10.21203/rs.3.rs-4183897/v1
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A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators

Mahindra Rautela,
Alan Williams,
Alexander Scheinker

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… Show more

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