Oct 20 – 25, 2019
America/Mexico_City timezone

Machine learning in accelerator physics: applications at the CERN Large Hadron Collider

Oct 25, 2019, 11:00 AM
Oral Plenary


Frederik Van Der Veken (University of Malta (MT))


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, machine learning techniques are being scrutinised for the advanced analysis of numerical simulations data, in view of improving our models of dynamic aperture evolution.

Primary authors

Belen Maria Salvachua Ferrando (CERN) Elena Fol (Johann-Wolfgang-Goethe Univ. (DE)) Fred Blanc (EPFL - Ecole Polytechnique Federale Lausanne (CH)) Frederik Van Der Veken (University of Malta (MT)) Gabriella Azzopardi (University of Malta (MT)) Gianluca Valentino (University of Malta (MT)) Loic Thomas Davies Coyle (EPFL - Ecole Polytechnique Federale Lausanne (CH)) Massimo Giovannozzi (CERN) Michael Schenk (EPFL - Ecole Polytechnique Federale Lausanne (CH)) Rogelio Tomas Garcia (CERN) Stefano Redaelli (CERN) Dr Tatiana Pieloni (EPF Lausanne)

Presentation materials