Particle Accelerators and Machine Learning
Particle accelerator facilities have a wide range of operational needs when it comes to tuning, optimisation, and control. At the Large Hadron Collider (LHC) at CERN reducing the risks related to the high beam power by reducing the beam losses will lead to increase in particle collision rates and a deeper
understanding of the physics mechanisms. In order to meet these sorts of demands, particle accelerators rely on interactions with control systems, on fine-tuning of machine settings by operators, online optimisation routines, and on databases of previous settings that were known to be optimal for some desired operating condition. We aim to bring Machine Learning (ML) to particle accelerator operation, in order to increase the performance. Each of the mentioned operational needs have corresponding ML-based approaches that could be used to supplement the existing workflows. In addition, new HL-LHC and FCC designs will be proposed based on the LHC findings and prepare for more effective novel FCC operation.
Minimise beam losses, better control of accelerator parameters, prevent unnecessary machine interruptions.
We propose to gather massive amount of accelerator data in collaboration with the LHC Operation groups to evaluate automatic and semi-automatic ways to optimise and steer the overall collider set-up and define the strategy for the operational aspects of the future projects (i.e. HL-LHC and FCC). In parallel to operational data accumulated during the physics runs, time will be devoted for machine development studies for testing the robustness of the developed models used for an automatised optimisation of the collider performances In dedicated experiments we will request the trained model to predict and set new parameters to improve the beam lifetimes in the LHC. Depending on the results obtained a continuation of the study and the extension of the models to other accelerators of the CERN complex and to future machine (HL-LHC) will be a natural path for a continuation of the collaboration.
We are aiming to implement the paradigm of digital twins, i.e. a virtual representation of the real world accelerator. At the same time, this could create new virtual and augmented reality opportunities, which will certainly be a big theme in the implementation of the future FCC.
LHC Fill, Beam Modes (from Wyszkowski, Przemysław Michał. ESB application for effective synchronization of large volume measurements data. Diss. AGH-UST, Cracow, 2011.)
Schematic view of the LHC with two-beam design (from Brüning O, Burkhardt H, Myers S. The large hadron collider. Progress in Particle and Nuclear Physics. 2012 Jul 1;67(3):705-34)
- L. Coyle, F. Blanc, T. Pieloni, M. Schenk, X. Buffat, M. Solfaroli Camillocci, J. Wenninger, E. Krymova, G. Obozinski, “Detection and Classification of Collective Beam Behaviour in the LHC”, in Proc. 12th Int. Particle Accelerator Conf. (IPAC’21), Campinas, Brazil, May 2021, pp. 1923-1926. doi:10.18429/JACoW-IPAC2021- THPAB260.
- M. Schenk, L. Coyle, M. Giovannozzi, A. Mereghetti, T. Pieloni, E. Krymova, G. Obozinski, “Modeling Particle Stability Plots for Accelerator Optimization Using Adaptive Sampling”, in Proc. 12th Int. Particle Accelerator Conf. (IPAC’21), Campinas, Brazil, May 2021, pp. 1923-1926. doi:10.18429/JACoW-IPAC2021-TUPAB216.
G. Apollinari et al. (including T. Pieloni) “High-Luminosity Large Hadron Collider (HL- LHC): Preliminary Design Report – Chapter 2: Machine Layout and Performances” Preliminary Design Report
L. Coyle, “Machine learning applications for hadron colliders: LHC lifetime optimization and designing Future Circular Colliders”, presented at the 2018 Swiss Physics Society Meeting at EPFL Annual meeting of the Swiss Physical Society 2018 2752252/SPS_talk.pdf