Classifier-pruned Bayesian optimization for particle accelerator tuning: Exploring temporally structured manifold of 6D beam phase space

Abstract:Complex dynamical systems, such as particle accelerators, often require intricate and time-consuming tuning procedures to achieve optimal performance. In many cases, these procedures must also estimate the optimal system parameters governing the dynamics of a spatiotemporal beam, making the task a high-dimensional optimization problem. To address this, we propose a Classifier-pruned Bayesian Optimization-based Latent space Tuner (CBOL-Tuner), a framework for efficient exploration within a temporally-structured latent manifold of 6D beam phase space. The CBOL-Tuner integrates a conditional variational autoencoder for latent space representation, a long short-term memory network for temporal dynamics, a lightweight neural network for parameter estimation, and a classifier-pruned Bayesian optimizer to adaptively search and filter the latent space for optimal solutions.

Machine Learning (cs.LG)

arXiv:2412.01748 [cs.LG]

(or arXiv:2412.01748v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2412.01748 Focus to learn more arXiv-issued DOI via DataCite

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