PoS - Proceedings of Science
Volume 372 - Artificial Intelligence for Science, Industry and Society (AISIS2019) - Day 5
Machine learning in accelerator physics: applications at the CERN Large Hadron Collider
F. Van Der Veken,* G. Azzopardi, F. Blanc, L. Coyle, E. Fol, M. Giovannozzi, T. Pieloni, S. Redaelli, B.M. Salvachua Ferrando, M. Schenk, R. Tomas Garcia, G. Valentino
*corresponding author
Full text: pdf
Pre-published on: July 10, 2020
Published on:
Abstract
With the advent of Machine Learning a few decades ago, Science and Engineering have had new powerful tools at their disposal. Particularly in the domain of particle physics, Machine Learning techniques have become an essential part in the analysis of data from particle collisions. Accelerator physics, however, only recently discovered the possibilities of using these tools to improve its analysis. In different laboratories worldwide, several activities are being carried out, typically in view of providing new insights to beam dynamics in circular accelerators. This is, for instance, the case for the CERN Large Hadron Collider, where, since a few years, exploratory studies are being carried out, covering a broad range of topics. These include the optimisation of the collimation system, the anomaly detection of beam position monitors, analysis of optimal correction tools for linear optics, lifetime and performance optimisation, and detection of hidden correlations in the huge data set of beam dynamics observables collected during the LHC Run 2. Furthermore, very recently, ML techniques are being scrutinised for the advanced analysis of numerical simulations data, in view of improving our models of dynamic aperture evolution.
How to cite

Metadata are provided both in "article" format (very similar to INSPIRE) as this helps creating very compact bibliographies which can be beneficial to authors and readers, and in "proceeding" format which is more detailed and complete.

Open Access
Creative Commons LicenseCopyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.