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RIS citation export for WEVIR12: Machine Learning Techniques for Optics Measurements and Corrections

AU  - Fol, E.
AU  - Franchetti, G.
AU  - Tomás, R.
ED  - Schaa, Volker RW
TI  - Machine Learning Techniques for Optics Measurements and Corrections
J2  - Proc. of IPAC2020, Caen, France, 10-15 May 2020
CY  - Caen, France
T2  - International Particle Accelerator Conference
T3  - 11
LA  - english
AB  - Recently, various efforts have presented Machine Learning (ML) as a powerful tool for solving accelerator problems. In the LHC a decision tree-based algorithm has been applied to detect erroneous beam position monitors demonstrating successful results in operation. Supervised regression models trained on simulations of LHC optics with quadrupole errors promise to significantly speed-up optics corrections by finding local errors in the interaction regions. The implementation details, results and future plans for these studies will be discussed following a brief introduction to ML concepts and its suitability to different problems in the domain of accelerator physics.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 61
EP  - 66
KW  - optics
KW  - quadrupole
KW  - controls
KW  - simulation
KW  - network
DA  - 2020/10
PY  - 2020
SN  - 2673-5490
SN  - 978-3-95450-213-4
DO  - doi:10.18429/JACoW-IPAC2020-WEVIR12
UR  - http://jacow.org/ipac2020/papers/wevir12.pdf
ER  -