The Joint Accelerator Conferences Website (JACoW) is an international collaboration that publishes the proceedings of accelerator conferences held around the world.
TY - CONF 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 -