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Bridging mathematics and physics: models of the evolution of dynamic aperture in hadron colliders and applications to LHC
/ Van der Veken, Frederik (CERN ; Malta U.) ; Bazzani, Armando (U. Bologna, DIFA ; INFN, Bologna) ; Giovannozzi, Massimo (CERN) ; Maclean, Ewen Hamish (CERN ; Malta U.) ; Montanari, Carlo Emiglio (U. Bologna, DIFA) ; Goethem, Wietse Van (CERN ; Antwerp U.)
When designing a high-energy, circular accelerator, like the upcoming High-Luminosity LHC or the future FCC, it is essential to have a reliable estimate of the expected beam losses and beam lifetime. A good prediction of the beam losses is essential to anticipate potential issues leading to quenches of the superconducting magnets or damage to the collimation system, while the beam lifetime is in direct relation to luminosity and, hence, to the overall performance of the accelerator. [...]
SISSA, 2020 - 7 p.
- Published in : PoS EPS-HEP2019 (2020) 023
Fulltext from publisher: PDF;
In : European Physical Society Conference on High Energy Physics (EPS-HEP) 2019, Ghent, Belgium, 10 - 17 Jul 2019, pp.023
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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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Machine learning for beam dynamics studies at the CERN Large Hadron Collider
/ Arpaia, P. (Naples U.) ; Azzopardi, G. (CERN) ; Blanc, F. (Ecole Polytechnique, Lausanne) ; Bregliozzi, G. (CERN) ; Buffat, X. (CERN) ; Coyle, L. (Ecole Polytechnique, Lausanne ; CERN) ; Fol, E. (Frankfurt U. ; CERN) ; Giordano, F. (Naples U. ; CERN) ; Giovannozzi, M. (CERN) ; Pieloni, T. (Ecole Polytechnique, Lausanne ; CERN) et al.
Machine learning entails a broad range of techniques that have been widely used in Science and Engineering since decades. High-energy physics has also profited from the power of these tools for advanced analysis of colliders data. [...]
arXiv:2009.08109.-
2021-01-01 - 14 p.
- Published in : Nucl. Instrum. Methods Phys. Res., A 985 (2021) 164652
Fulltext: PDF; Fulltext from publisher: PDF;
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Hamiltonian theory of the crossing of the $2 Q_x -2 Q_y=0$ nonlinear coupling resonance
/ Bazzani, A. (U. Bologna, DIFA ; INFN, Bologna) ; Capoani, F. (U. Bologna, DIFA ; INFN, Bologna ; CERN) ; Giovannozzi, M. (CERN)
In a recent paper, the adiabatic theory of Hamiltonian systems was successfully applied to study the crossing of the linear coupling resonance, $Q_x-Q_y=0$. A detailed explanation of the well-known phenomena that occur during the resonance-crossing process, such as emittance exchange and its dependence on the adiabaticity of the process was obtained. [...]
arXiv:2208.11519.-
2022-10-03 - 21 p.
- Published in : Phys. Rev. Accel. Beams 25 (2022) 104001
Fulltext: 2208.11519 - PDF; Publication - PDF;
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Error characterization and calibration of real-time magnetic field measurement systems
/ Grech, Christian (CERN ; Malta U.) ; Amodeo, Maria (Turin Polytechnic ; CERN) ; Beaumont, Anthony (CERN) ; Buzio, Marco (CERN) ; Capua, Vincenzo Di (CERN ; Naples U.) ; Giloteaux, David (CERN) ; Sammut, Nicholas (CERN ; Malta U.) ; Wallbank, Joseph Vella (CERN ; Malta U.)
In synchrotrons at the European Organization for Nuclear Research (CERN), magnetic measurement systems known as B-trains measure the magnetic field in the main bending magnets in real-time, and transmit this signal for the control of the synchrotron’s RF accelerating cavities, magnet power converter and beam monitoring systems. This work presents an assessment of the capabilities and performance of the new FIRESTORM (Field In REal-time STreaming from Online Reference Magnets) system as part of the first phase of commissioning. [...]
2021 - 9 p.
- Published in : Nucl. Instrum. Methods Phys. Res., A 990 (2021) 164979
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