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RIS citation export for THZE1: Machine Learning-Based Longitudinal Phase Space Prediction of Particle Accelerators

TY  - UNPB
AU  - Emma, C.
ED  - Biedron, Sandra
ED  - Simakov, Evgenya
ED  - Milton, Stephen
ED  - Anisimov, Petr M.
ED  - Schaa, Volker R.W.
TI  - Machine Learning-Based Longitudinal Phase Space Prediction of Particle Accelerators
J2  - Proc. of NAPAC2022, Albuquerque, NM, USA, 07-12 August 2022
CY  - Albuquerque, NM, USA
T2  - International Particle Accelerator Conference
T3  - 5
LA  - english
AB  - The author would report on the application of machine learning (ML) methods for predicting the longitudinal phase space (LPS) distribution of particle accelerators. The approach consists of training a ML-based virtual diagnostic to predict the LPS using only nondestructive linac and e-beam measurements as inputs.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
ER  -