Machine learning-based longitudinal phase space prediction of particle accelerators
We report on the application of machine learning (ML) methods for predicting the longitudinal phase space (LPS) distribution of particle accelerators. Our approach consists of training a ML-based virtual diagnostic to predict the LPS using only nondestructive linac and e-beam measurements as inputs....
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2018-11-01
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| Series: | Physical Review Accelerators and Beams |
| Online Access: | http://doi.org/10.1103/PhysRevAccelBeams.21.112802 |