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RIS citation export for FRBL03: A Literature Review on the Efforts Made for Employing Machine Learning in Synchrotrons

AU  - Khaleghi, A.
AU  - Aghaei, Z.
AU  - Ahmad Mehrabi, F.
AU  - Akbari, M.
AU  - Haedar, H.
AU  - Iman, I.
AU  - Jafarzadeh, M.
AU  - Mahmoudi, K.
AU  - Navidpour, P.
ED  - Furukawa, Kazuro
ED  - Yan, Yingbing
ED  - Leng, Yongbin
ED  - Chen, Zhichu
ED  - Schaa, Volker R.W.
TI  - A Literature Review on the Efforts Made for Employing Machine Learning in Synchrotrons
J2  - Proc. of ICALEPCS2021, Shanghai, China, 14-22 October 2021
CY  - Shanghai, China
T2  - International Conference on Accelerator and Large Experimental Physics Control Systems
T3  - 18
LA  - english
AB  - Using machine learning (ML) in various contexts is in-creasing due to advantages such as automation for every-thing, trends and pattern identification, highly error-prone, and continuous improvement. Even non-computer experts are trying to learn simple programming languages like Python to implement ML models on their data. De-spite the growing trend towards ML, no study has re-viewed the efforts made on using ML in synchrotrons to our knowledge. Therefore, we are examining the efforts made to use ML in synchrotrons to achieve benefits like stabilizing the photon beam without the need for manual calibrations of measures that can be achieved by reducing unwanted fluctuations in the widths of the electron beams that prevent experimental noises obscured measurements. Also, the challenges of using ML in synchrotrons and a short synthesis of the reviewed articles were provided. The paper can help related experts have a general famil-iarization regarding ML applications in synchrotrons and encourage the use of ML in various synchrotron practices. In future research, the aim will be to provide a more com-prehensive synthesis with more details on how to use the ML in synchrotrons.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 1039
EP  - 1046
KW  - synchrotron
KW  - experiment
KW  - software
KW  - real-time
KW  - electron
DA  - 2022/03
PY  - 2022
SN  - 2226-0358
SN  - 978-3-95450-221-9
DO  - doi:10.18429/JACoW-ICALEPCS2021-FRBL03
UR  - https://jacow.org/icalepcs2021/papers/frbl03.pdf
ER  -