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RIS citation export for WEPGW064: Machine Learning Application in Bunch Longitudinal Phase Measurement

TY  - CONF
AU  - Xu, X.Y.
AU  - Leng, Y.B.
AU  - Zhou, Y.M.
ED  - Boland, Mark
ED  - Tanaka, Hitoshi
ED  - Button, David
ED  - Dowd, Rohan
ED  - Schaa, Volker RW
ED  - Tan, Eugene
TI  - Machine Learning Application in Bunch Longitudinal Phase Measurement
J2  - Proc. of IPAC2019, Melbourne, Australia, 19-24 May 2019
CY  - Melbourne, Australia
T2  - International Particle Accelerator Conference
T3  - 10
LA  - english
AB  - High resolution bunch-by-bunch longitudinal phase measurement has been realized at Shanghai Synchrotron Radiation Facility (SSRF). In order to fully exploit the potency of the bunch phase monitor, the transient state during injection is being further studied. A longitudinal phase fitting method was used to study the synchrotron damping oscillation in injection events, where we can get the energy offsets between the injector and the storage ring, refilled bunch arrived time and the synchrotron damping time. However, manual multi-parameter fitting of nonlinear functions is awfully complex and slow. Machine learning algorithms, such as gradient descent and artificial neural network (ANN) is more suitable to do this fitting. Through these methods, we can quickly obtain more accurate fitting parameters and further realize online measurement of the refilled charge arrived time, energy offsets between the injector and storage ring, and the synchrotron damping time.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 2625
EP  - 2628
KW  - network
KW  - synchrotron
KW  - damping
KW  - SRF
KW  - injection
DA  - 2019/06
PY  - 2019
SN  - 978-3-95450-208-0
DO  - DOI: 10.18429/JACoW-IPAC2019-WEPGW064
UR  - http://jacow.org/ipac2019/papers/wepgw064.pdf
ER  -