Towards latent space evolution of spatiotemporal dynamics of six-dimensional phase space of charged particle beams
arxiv(2024)
Abstract
Addressing the charged particle beam diagnostics in accelerators poses a
formidable challenge, demanding high-fidelity simulations in limited
computational time. Machine learning (ML) based surrogate models have emerged
as a promising tool for non-invasive charged particle beam diagnostics. Trained
ML models can make predictions much faster than computationally expensive
physics simulations. In this work, we have proposed a temporally structured
variational autoencoder model to autoregressively forecast the spatiotemporal
dynamics of the 15 unique 2D projections of 6D phase space of charged particle
beam as it travels through the LANSCE linear accelerator. In the model, VAE
embeds the phase space projections into a lower dimensional latent space. A
long-short-term memory network then learns the temporal correlations in the
latent space. The trained network can evolve the phase space projections across
further modules provided the first few modules as inputs. The model predicts
all the projections across different modules with low mean squared error and
high structural similarity index.
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