ICALEPCS2021 - List of Authors (Jafarzadeh, M.)

Paper	Title	Page
FRBL03	A Literature Review on the Efforts Made for Employing Machine Learning in Synchrotrons	1039
	A. Khaleghi, Z. Aghaei, H. Haedar, I. Iman, K. Mahmoudi IKIU, Qazvin, Iran F. Ahmad Mehrabi, M. Akbari, M. Jafarzadeh, A. Khaleghi, P. Navidpour ILSF, Tehran, Iran
	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.
	Slides FRBL03 [1.681 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-FRBL03
About •	Received ※ 10 October 2021 Revised ※ 20 October 2021 Accepted ※ 20 November 2021 Issue date ※ 12 March 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

A Literature Review on the Efforts Made for Employing Machine Learning in Synchrotrons

1039

A. Khaleghi, Z. Aghaei, H. Haedar, I. Iman, K. Mahmoudi
IKIU, Qazvin, Iran
F. Ahmad Mehrabi, M. Akbari, M. Jafarzadeh, A. Khaleghi, P. Navidpour
ILSF, Tehran, Iran

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.

Slides FRBL03 [1.681 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-FRBL03

About •

Received ※ 10 October 2021 Revised ※ 20 October 2021 Accepted ※ 20 November 2021 Issue date ※ 12 March 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)