Beams Document 6422-v1

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Recent applications of machine learning for particle accelerator control

Document #:
Beams-doc-6422-v1
Document type:
Talk
Submitted by:
Arun Saini
Updated by:
Arun Saini
Document Created:
05 Jun 2018, 22:10
Contents Revised:
05 Jun 2018, 22:10
Metadata Revised:
05 Jun 2018, 22:10
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Abstract:
Machine learning (ML) is currently experiencing a renaissance, thanks to recent computational, theoretical, and practical advances in the field. ML techniques are now technologically mature enough to be brought to bear on the particular problems of optimization and control of particle accelerator systems. Recently, an ICFA Workshop was held at SLAC on this topic: "Machine Learning Applications for Particle Accelerators" (https://conf.slac.stanford.edu/icfa-ml-2018/). I will give an overview of this ICFA workshop, emphasizing recent research that yielded real performance improvements for accelerator laboratories. I will also discuss current R&D and new research directions.
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Keywords:
Machine Learning
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