LHC Optics Measurement and Correction Software Progress and Plans

Tomás, Rogelio; Cardona, Javier; Carlier, Felix Simon; Portugal, Jaime Maria Coello de; Dilly, Joschua; Fol, Elena; Fuster, J.; Garcia-Tabares, Ana; Gonzalez-Iglesias, D.; Hoekstra, Ronnie; Hofer, Michaël; Maclean, Ewen; Malina, Lukáš; Persson, Tobias; Rodriguez, Y.; Skowroński, Piotr; Spitznagel, Martin; Wegscheider, Andreas; Wenninger, J.

doi:10.18429/jacow-ipac2019-wepgw116

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“…Cleaning of optics measurements data refers to reduction of the noise of the obtained BPM signal, as well as removing anomalous signal in turn-by-turn data, including detection of faulty signal artefact in later stages of data processing such as harmonic analysis. Several numerical thresholds as well as a cleaning technique based on Singular Value Decomposition (SVD) are used in order to remove faulty signals [48,84]. Since non-physical values still can be observed in the optics functions computed from the cleaned data, it is not uncommon that additional manual cleaning of harmonic analysis data is required, followed by repeating the optics computation.…”

Section: General Conceptmentioning

confidence: 99%

Application of machine learning in beam optics measurements and corrections

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The present research in high energy physics as well as in the nuclear physics requires the use of more powerful and complex particle accelerators to provide high luminosity, high intensity, and high brightness beams to experiments. With the increased technolo- gical complexity of accelerators, meeting the demand of experimenters necessitates a blend of accelerator physics with technology. The problem becomes severe when optimization of beam quality has to be provided in accelerator systems with thousands of free parameters including strengths of quadrupoles, sextupoles, RF voltages, etc. Machine learning methods and concepts of artificial intelligence are considered in various industry and scientific branches, and recently, these methods are used in high energy physics mainly for experiments data analysis. In Accelerator Physics the machine learning approach has not found a wide application yet, and in general the use of these methods is carried out without a deep understanding on their effectiveness with respect to more traditional schemes or other alternative approaches. The purpose of this PhD research is to investigate the methods of machine learning applied to accelerator optimization, accelerator control and in particular on optics measurements and corrections. Optics correction, maximization of acceptance, and simultaneous control of various accelerator components such as focusing magnets is a typical accelerator scenario. The effectiven- ess of machine learning methods in a complex system such as the Large Hadron Collider, which beam dynamics exhibits nonlinear response to machine settings is the core of the study. This work presents successful application of several machine learning techniques such as clustering, decision trees, linear multivariate models and neural networks to beam optics measurements and corrections at the LHC, providing the guidelines for incorporation of machine learning techniques into accelerator operation and discussing future opportunities and potential work in this field.

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Section: General Conceptmentioning

confidence: 99%

Application of machine learning in beam optics measurements and corrections

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LHC Optics Measurement and Correction Software Progress and Plans

Cited by 1 publication

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Application of machine learning in beam optics measurements and corrections

Application of machine learning in beam optics measurements and corrections

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