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Using Machine Learning to Improve Dynamic Aperture Estimates
/ Van der Veken, Frederik F (CERN ; Malta U.) ; Giovannozzi, Massimo (CERN) ; Maclean, Ewen H (CERN ; Malta U.) ; Montanari, Carlo Emilio (Bologna U. ; CERN) ; Valentino, Gianluca (Malta U. ; CERN)
The dynamic aperture (DA) is an important concept in the study of nonlinear beam dynamics. Several analytical models used to describe the evolution of DA as a function of time, and to extrapolate to realistic time scales that would not be reachable otherwise due to computational limitations, have been successfully developed. [...]
JACoW, 2021 - 4 p.
- Published in : JACoW IPAC 2021 (2021) 134-137
Fulltext: PDF;
In : 12th International Particle Accelerator Conference (IPAC 2021), Online, 24 - 28 May 2021, pp.134-137
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Testing the Global Diffusive Behaviour of Beam-Halo Dynamics at the CERN LHC Using Collimator Scans
/ Montanari, Carlo Emilio (Bologna U.) ; Bazzani, Armando (Bologna U.) ; Giovannozzi, Massimo (CERN) ; Gorzawski, Arkadiusz (Lund U.) ; Redaelli, Stefano (CERN)
In superconducting circular particle accelerators, controlling beam losses is of paramount importance for ensuring optimal machine performance and an efficient operation. To achieve the required level of understanding of the mechanisms underlying beam losses, models based on global diffusion processes have recently been studied and proposed to investigate the beam-halo dynamics. [...]
2022 - 4 p.
- Published in : JACoW IPAC 2022 (2022) 172-175
Fulltext: PDF;
In : 13th International Particle Accelerator Conference (IPAC 2022), Bangkok, Thailand, 12 - 17 Jun 2022, pp.172-175
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Determination of the Phase-Space Stability Border with Machine Learning Techniques
/ Van der Veken, Frederik (CERN) ; Akbari, Runa (CERN) ; Bogaert, Michiel (CERN) ; Fol, Elena (CERN) ; Giovannozzi, Massimo (CERN) ; Lowyck, Amy (CERN) ; Montanari, Carlo Emilio (CERN) ; Van Goethem, Wietse (CERN)
The dynamic aperture (DA) of a hadron accelerator is represented by the volume in phase space that exhibits bounded motion, where we disregard any disconnected parts that could be due to stable islands. To estimate DA in numerical simulations, it is customary to sample a set of initial conditions using a polar grid in the transverse planes, featuring a limited number of angles and using evenly distributed radial amplitudes. [...]
2022 - 4 p.
- Published in : JACoW IPAC 2022 (2022) 183-186
Fulltext: PDF;
In : 13th International Particle Accelerator Conference (IPAC 2022), Bangkok, Thailand, 12 - 17 Jun 2022, pp.183-186
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Using Dynamic Indicators for Probing Single-Particle Stability in Circular Accelerators
/ Montanari, Carlo Emilio (Bologna U. ; CERN) ; Bazzani, Armando (Bologna U.) ; Giovannozzi, Massimo (CERN) ; Turchetti, Giorgio (Bologna U.)
Computing the long-term behaviour of single-particle motion is a numerically intensive process, as it requires a large number of initial conditions to be tracked for a large number of turns to probe their stability. A possibility to reduce the computational resources required is to provide indicators that can efficiently detect chaotic motion, which are considered precursors to unbounded motion. [...]
2022 - 4 p.
- Published in : JACoW IPAC 2022 (2022) 168-171
Fulltext: PDF;
In : 13th International Particle Accelerator Conference (IPAC 2022), Bangkok, Thailand, 12 - 17 Jun 2022, pp.168-171
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SixTrack Version 5: Status and new developments
/ De Maria, Riccardo (CERN) ; Andersson, Joel (CERN) ; Dalena, Barbara (IRFU, Saclay) ; Field, Laurence (CERN) ; Giovannozzi, Massimo (CERN) ; Hermes, Pascal (CERN) ; Hoimyr, Nils (CERN) ; Iadarola, Giovanni (CERN) ; Kostoglou, Sofia (CERN) ; Maclean, Ewen (CERN ; U. Malta) et al.
SixTrack Version 5 is a major SixTrack release that introduces new features, with improved integration of the existing ones, and extensive code restructuring. New features include dynamic-memory management, scattering-routine integration, a new initial-condition module, and reviewed post-processing methods. [...]
CERN-ACC-2019-117.-
2019 - 4 p.
- Published in : 10.18429/JACoW-IPAC2019-WEPTS043
Preprint: PDF;
In : 10th International Particle Accelerator Conference, Melbourne, Australia, 19 - 24 May 2019, pp.WEPTS043
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Optics Measurements and Correction Plans for the HL-LHC
/ Persson, Tobias (CERN) ; Buffat, Xavier (CERN) ; Carlier, Felix (CERN) ; Coello de Portugal, Jaime Maria (PSI, Villigen) ; De Maria, Riccardo (CERN) ; Dilly, Joschua (CERN) ; Fol, Elena (CERN) ; Gamba, Davide (CERN) ; Garcia Morales, Hector (CERN) ; García-Tabarés Valdivieso, Ana (CERN) et al.
The High Luminosity LHC (HL-LHC) will require stringent optics correction to operate safely and deliver the design luminosity to the experiments. In order to achieve this, several new methods for optics correction have been developed. [...]
2021 - 4 p.
- Published in : 10.18429/JACoW-IPAC2021-WEPAB026
Fulltext: PDF;
In : 12th International Particle Accelerator Conference (IPAC 2021), Online, Br, 24 - 28 May 2021, pp.2656-2659
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Probing the Forced Dynamic Aperture in the LHC at Top Energy Using AC Dipoles
/ Carlier, Felix (CERN ; Nikhef, Amsterdam) ; Giovannozzi, Massimo (CERN) ; Maclean, Ewen (CERN) ; Persson, Tobias (CERN) ; Tomás, Rogelio (CERN)
Measurements of the dynamic aperture in colliders are a common method to ensure machine performance and offer an insight in the nonlinear content of the machine. Such direct measurements are very challenging for the LHC and High Luminosity LHC. [...]
CERN-ACC-2018-081.-
2018 - 4 p.
- Published in : 10.18429/JACoW-IPAC2018-MOPMF033
Fulltext: PDF;
In : 9th International Particle Accelerator Conference, Vancouver, Canada, 29 Apr - 4 May 2018, pp.MOPMF033
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Application of machine learning techniques at the CERN Large Hadron Collider
/ Van der Veken, Frederik (CERN ; Malta U.) ; Azzopardi, Gabriella (CERN ; Malta U.) ; Blanc, Fred (Ecole Polytechnique, Lausanne) ; Coyle, Loic (CERN ; Ecole Polytechnique, Lausanne) ; Fol, Elena (CERN ; Goethe U., Frankfurt (main)) ; Giovannozzi, Massimo (CERN) ; Pieloni, Tatiana (Ecole Polytechnique, Lausanne ; CERN) ; Redaelli, Stefano (CERN) ; Rivkin, Leonid (Ecole Polytechnique, Lausanne ; PSI, Villigen) ; Salvachua, Belen (CERN) et al.
Machine learning techniques have been used extensively in several domains of Science and Engineering for decades. These powerful tools have been applied also to the domain of high-energy physics, in the analysis of the data from particle collisions, for years already. [...]
SISSA, 2020 - 9 p.
- Published in : PoS EPS-HEP2019 (2020) 006
Fulltext from publisher: PDF;
In : European Physical Society Conference on High Energy Physics (EPS-HEP) 2019, Ghent, Belgium, 10 - 17 Jul 2019, pp.006
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