JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
@inproceedings{fol:ipac2022-mopopt047,
author = {E. Fol and J.F. Cardona and F.S. Carlier and J. Dilly and M. Hofer and J. Keintzel and M. Le Garrec and E.H. Maclean and T.H.B. Persson and F. Soubelet and R. Tomás García and A. Wegscheider},
% author = {E. Fol and J.F. Cardona and F.S. Carlier and J. Dilly and M. Hofer and J. Keintzel and others},
% author = {E. Fol and others},
title = {{Experimental Demonstration of Machine Learning Application in LHC Optics Commissioning}},
booktitle = {Proc. IPAC'22},
% booktitle = {Proc. 13th International Particle Accelerator Conference (IPAC'22)},
pages = {359--362},
eid = {MOPOPT047},
language = {english},
keywords = {optics, quadrupole, MMI, simulation, diagnostics},
venue = {Bangkok, Thailand},
series = {International Particle Accelerator Conference},
number = {13},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {07},
year = {2022},
issn = {2673-5490},
isbn = {978-3-95450-227-1},
doi = {10.18429/JACoW-IPAC2022-MOPOPT047},
url = {https://jacow.org/ipac2022/papers/mopopt047.pdf},
abstract = {{Recently, we conducted successful studies on the suitability of machine learning (ML) methods for optics measurements and corrections, incorporating novel ML-based methods for local optics corrections and reconstruction of optics functions. After performing extensive verifications on simulations and past measurement data, the newly developed techniques became operational in the LHC commissioning 2022. We present the experimental results obtained with the ML-based methods and discuss future improvements. Besides, we also report on improving the Beam Position Monitor (BPM) diagnostics with the help of the anomaly detection technique capable to identify malfunctioning BPMs along with their possible fault causes.}},
}