Machine Learning Applied to the Analysis of Nonlinear Beam Dynamics Simulations for the CERN Large Hadron Collider and Its Luminosity Upgrade

Giovannozzi, M.; Maclean, Ewen; Montanari, C.; Valentino, Gianluca; Veken, Frederik F. Van der

doi:10.3390/info12020053

Cited by 7 publications

(3 citation statements)

References 35 publications

(36 reference statements)

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“…In this way, we could use the ESN predictions to replace the tracking in the test set. This result is in line with what was found in [45], i.e. that the addition of synthetic points obtained by using Gaussian Processes improved the quality of the fitted SL model.…”

Section: The Sl-esn Modelsupporting

confidence: 92%

Ensemble Reservoir Computing for Dynamical Systems: Prediction of Phase-Space Stable Region for Hadron Storage Rings

Maxime¹,

Dalena²,

Bonaventura³

et al. 2023

Preprint

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We investigate the ability of an ensemble reservoir computing approach to predict the long-term behaviour of the phase-space region in which the motion of charged particles in hadron storage rings is bounded, the so-called dynamic aperture. Currently, the calculation of the phasespace stability region of hadron storage rings is performed through direct computer simulations, which are resource-and time-intensive processes. Echo State Networks (ESN) are a class of recurrent neural networks that are computationally effective, since they avoid backpropagation and require only cross-validation. Furthermore, they have been proven to be universal approximants of dynamical systems. In this paper, we present the performance reached by ESN based on an ensemble approach for the prediction of the phase-space stability region and compare it

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Section: The Sl-esn Modelsupporting

confidence: 92%

Ensemble Reservoir Computing for Dynamical Systems: Prediction of Phase-Space Stable Region for Hadron Storage Rings

Maxime¹,

Dalena²,

Bonaventura³

et al. 2023

Preprint

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“…In recent years, Machine Learning (ML) techniques have emerged as a promising approach to accelerate DA evaluation (see, e.g. [8,9,10,11]). By training a model on a large data set of simulated initial conditions, an ML algorithm can learn the complex mapping between the initial conditions and the angular DA (see below) and provide a fast and accurate prediction of the angular DA for new sets of initial conditions and machine configurations.…”

Section: Introductionmentioning

confidence: 99%

Accelerating Dynamic Aperture Evaluation Using Deep Neural Networks

Croce,

Giovannozzi,

Pieloni

et al. 2024

J. Phys.: Conf. Ser.

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The Dynamic Aperture is an important concept for the study of non-linear beam dynamics in a circular accelerator. The DA is defined as the extent of the phase-space region in which the particle’s motion remains bounded over a finite number of turns. Such a region is shaped by the imperfections in the magnetic fields, beam-beam effects, electron lens, electron clouds, and other non-linear effects. The study of the DA provides insight into the mechanisms driving the time evolution of beam losses, which is essential for the operation of existing circular accelerators, such as the CERN Large Hadron Collider, as well as for the design of future ones. The standard approach to numerical evaluation of the DA relies on the ability to accurately track initial conditions, distributed in phase space, on a realistic time scale, and this is computationally demanding. To accelerate the angular DA calculation, we propose the use of a Machine Learning technique for the angular DA regression based on simulated HL-LHC data. We demonstrate the implementation of a Deep Neural Network model by measuring the time and assessing the performance of the angular DA regressor, as well as carrying out studies with various hardware architectures including CPU, GPU, and TPU.

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“…It is important to note that the vibration stability of structures is dependent on both the structure's dynamic behaviour and the ambient circumstances in which it is installed. Dynamic behaviour of the LHC was extensively researched in the past [5,6], however, there is still much to be done in the case of describing the effects of specific environmental conditions and unique events. The effects of a seismic waves are often devastating for the experiments at CERN, which results in time and money lost.…”

Section: Introductionmentioning

confidence: 99%

High Activity Earthquake Swarm Event Monitoring and Impact Analysis on Underground High Energy Physics Research Facilities

Ścisło

2022

Energies

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A seismic swarm is a series of earthquakes that occur in a small area over a short period of time. A sequence of earthquakes of this magnitude is unusual in Switzerland, and it is impossible to anticipate how it may unfold in the future. The seismic activity of such an event usually fades after a few days or weeks. Significantly greater earthquakes are likely to occur during the next several days, with up to a chance of 5 to 10%. For these reasons, the underground research facilities need tools to provide data on the impact of these events on their experiments. The paper presents the techniques implemented at The European Organization for Nuclear Research (CERN) to allow the tracking and monitoring of these unusual events. Additionally, the real effect of such an unusual event is presented together with the statistical approach to monitoring and effect evaluation. Considering the collision energy of the beams at 14 TeV, the energy stored in the magnets at 10 GJ (2400 kg of TNT), and the energy carried by the two beams at 724 MJ (173 kg of TNT), prolonged exposure to vibration close to or above the set alarm levels may result in serious safety issues. The presented evaluation of earthquake swarm impact on underground facilities together with the approach for data evaluation can be used for the design of future detectors and accelerators. Additionally, it provides tools for facilities users to present the data in an easy to understand way. This includes the Future Circular Collider, whose purpose is to significantly expand the energy and intensity frontiers of planned particle colliders, with the goal of reaching collision energies of 100 TeV in the quest for novel physics. As a result, even greater standards for beam size and stability will be required.

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Machine Learning Applied to the Analysis of Nonlinear Beam Dynamics Simulations for the CERN Large Hadron Collider and Its Luminosity Upgrade

Cited by 7 publications

References 35 publications

Ensemble Reservoir Computing for Dynamical Systems: Prediction of Phase-Space Stable Region for Hadron Storage Rings

Ensemble Reservoir Computing for Dynamical Systems: Prediction of Phase-Space Stable Region for Hadron Storage Rings

Accelerating Dynamic Aperture Evaluation Using Deep Neural Networks

High Activity Earthquake Swarm Event Monitoring and Impact Analysis on Underground High Energy Physics Research Facilities

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