Abstract
Magnetic field errors and misalignments cause optics perturbations, which can lead to machine safety issues and performance degradation. The correlation between magnetic errors and deviations of the measured optics functions from design can be used in order to build supervised learning models able to predict magnetic errors directly from a selection of measured optics observables. Extending the knowledge of errors in individual magnets offers potential improvements of beam control by including this information into optics models and corrections computation. Besides, we also present a technique for denoising and reconstruction of measurements data, based on autoencoder neural networks and linear regression. We investigate the usefulness of supervised machine learning algorithms for beam optics studies in a circular accelerator such as the LHC, for which the presented method has been applied in simulated environment, as well as on experimental data.
Highlights
Optics corrections are crucial for safe machine operation and reliably high performance in terms of luminosity balance between experiments
This allows to test how reliable prediction is on unseen data and to investigate the ability of the model to learn the physical correlations between the linear magnetic field errors and optics perturbations
The field errors in the triplet magnets produce the largest contribution to the optics perturbations, and their prediction is evaluated separately from the magnets in the arcs, to ensure the ability of the presented approach to reconstruct the most significant error source
Summary
Optics corrections are crucial for safe machine operation and reliably high performance in terms of luminosity balance between experiments. The corrections to be applied in the LHC are computed as a set of magnetic field strength changes—either in the so-called circuits (quadrupoles powered in series) or individual quadrupoles that can be trimmed independently. These methods allow to achieve unprecedentedly low β-beating [5,6]; the information about actual errors in individual magnets which caused the compensated perturbations remains unavailable. We demonstrate the ability of supervised regression models trained on a large number of LHC simulations to predict the individual quadrupole errors given the measured optics perturbations in one step for both beams simultaneously.
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