Operators of the primary particle accelerator at the U.S. Department of Energy’s Thomas Jefferson National Accelerator Facility are getting a new tool to help them quickly address issues that can prevent it from running smoothly. A new machine learning system has passed its first two-week test, correctly identifying glitchy accelerator components and the type of glitches they’re experiencing in near-real-time.
An analysis of the results of the first field test of the custom-built machine learning…
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