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AI Captures Particle Accelerator Behavior to Optimize Machine Performance
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AI Captures Particle Accelerator Behavior to Optimize Machine Performance
A team of data scientists and accelerator experts at Jefferson Lab, working in collaboration with university and DOE national laboratory partners, have developed novel machine learning (ML) techniques to transparently model the physics of CEBAF. Their goal is to provide operators with a reliable tool to anticipate how each cavity will behave during accelerator operation and tuning, helping them stay steps ahead of potential faults and minimizing
·Charlottesville, United States
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