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Prospects to Apply Machine Learning to Optimize the Operation of the Crystal Collimation System at the LHC / D'Andrea, Marco (CERN) ; Azzopardi, Gabriella (CERN) ; Di Castro, Mario (CERN) ; Matheson, Eloise (CERN) ; Mirarchi, Daniele (CERN) ; Redaelli, Stefano (CERN) ; Ricci, Gianmarco (CERN ; U. Rome La Sapienza (main)) ; Valentino, Gianluca (CERN ; Malta U.)
Crystal collimation relies on the use of bent crystals to coherently deflect halo particles onto dedicated collimator absorbers. This scheme is planned to be used at the LHC to improve the betatron cleaning efficiency with high-intensity ion beams. [...]
2022 - 4 p. - Published in : JACoW IPAC 2022 (2022) 1362-1365 Fulltext: PDF;
In : 13th International Particle Accelerator Conference (IPAC 2022), Bangkok, Thailand, 12 - 17 Jun 2022, pp.1362-1365

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Test of Machine Learning at the CERN LINAC4 / Kain, Verena (CERN) ; Bruchon, Niky (CERN) ; Hirlaender, Simon (CERN) ; Madysa, Nico (CERN) ; Skowroński, Piotr (CERN) ; Valentino, Gianluca (U. Malta) ; Vojskovic, Isabella (CERN)
The CERN H$^-$ linear accelerator, LINAC4, served as a test bed for advanced algorithms during the CERN Long Shutdown 2 in the years 2019/20. One of the main goals was to show that reinforcement learning with all its benefits can be used as a replacement for numerical optimization and as a complement to classical control in the accelerator control context. [...]
2022 - 5 p. - Published in : JACoW HB 2021 (2022) 181-185 Fulltext: PDF;
In : 64th ICFA Advanced Beam Dynamics Workshop on High Intensity and High Brightness Hadron Beams (HB 2021), Batavia, Illinois, United States, 4 - 9 Oct 2021, pp.181-185

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Application of reinforcement learning in the LHC tune feedback / Grech, Leander (Malta U. ; CERN) ; Valentino, Gianluca (Malta U. ; CERN) ; Alves, Diogo (CERN) ; Hirlaender, Simon (Jagiellonian U.)
The Beam-Based Feedback System (BBFS) was primarily responsible for correcting the beam energy, orbit and tune in the CERN Large Hadron Collider (LHC). A major code renovation of the BBFS was planned and carried out during the LHC Long Shutdown 2 (LS2). [...]
2022 - 14 p. - Published in : Front. Phys. 10 (2022) 929064 Fulltext: PDF;

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Renovation of the Beam-Based Feedback Controller in the LHC / Grech, Leander (CERN) ; Alves, Diogo (CERN) ; Calia, Andrea (CERN) ; Hostettler, Michael (CERN) ; Jackson, Stephen (CERN) ; Valentino, Gianluca (U. Malta) ; Wenninger, Jorg (CERN)
This work presents an extensive overview of the design choices and implementation of the Beam-Based Feedback System (BBFS) used in operation until the LHC Run 2. The main limitations of the BBFS are listed and a new design called BFCLHC, which uses the CERN Front-End Software Architecture (FESA), framework is proposed. [...]
2022 - 6 p. - Published in : JACoW ICALEPCS2021 (2022) 671-676 Fulltext: PDF;
In : 18th International Conference on Accelerator and Large Experimental Physics Control Systems, Online, 18 - 22 Oct 2021, pp.671-676

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LHC Collimation Controls System for Run III Operation / Azzopardi, Gabriella (CERN) ; Di Castro, Mario (CERN) ; Redaelli, Stefano (CERN) ; Salvachua, Belen (CERN) ; Solfaroli Camillocci, Matteo (CERN) ; Valentino, Gianluca (U. Malta)
The Large Hadron Collider (LHC) collimation system is designed to protect the machine against unavoidable beam losses. The collimation system for the LHC Run 3, starting in 2022, consists of more than 100 movable collimators located along the 27 km long ring and in the transfer lines. [...]
2022 - 6 p. - Published in : JACoW ICALEPCS2021 (2022) 888-893 Fulltext: PDF;
In : 18th International Conference on Accelerator and Large Experimental Physics Control Systems, Online, 18 - 22 Oct 2021, pp.888-893

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The Automatic LHC Collimator Beam-Based Alignment Software Package / Azzopardi, Gabriella (CERN) ; Salvachua, Belen (CERN) ; Valentino, Gianluca (U. Malta)
The Large Hadron Collider (LHC) at CERN makes use of a complex collimation system to protect its sensitive equipment from unavoidable beam losses. The collimators are positioned around the beam respecting a strict transverse hierarchy. [...]
2022 - 6 p. - Published in : JACoW ICALEPCS2021 (2022) 659-664 Fulltext: PDF;
In : 18th International Conference on Accelerator and Large Experimental Physics Control Systems, Online, 18 - 22 Oct 2021, pp.659-664

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REMOTE: Machine Learning for the LHC and future machines: applications for simulations and operation / Valentino, Gianluca (speaker) (University of Malta (MT))
Abstract At the CERN Large Hadron Collider (LHC), several ML applications were actively pursued in view of assessing their potential benefits before making them an integral part of the accelerator operations and controls. These applications range from anomaly detection to pattern recognition and advanced data analysis. [...]
2022 - 3652. Academic Training Lecture Regular Programme, 2021-2022 External link: Event details In : REMOTE: Machine Learning for the LHC and future machines: applications for simulations and operation

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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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Machine learning in accelerator physics: Applications at the CERN Large Hadron Collider / Van der Veken, Frederik (Malta U. ; CERN) ; Azzopardi, Gabriella (CERN) ; Blanc, Fred (Ecole Polytechnique, Lausanne) ; Coyle, Loic (Ecole Polytechnique, Lausanne ; CERN) ; Fol, Elena (CERN ; Frankfurt U.) ; Giovannozzi, Massimo (CERN) ; Pieloni, Tatiana (Ecole Polytechnique, Lausanne ; CERN) ; Redaelli, Stefano (CERN) ; Salvachua Ferrando, Belen Maria (CERN) ; Schenk, Michael (CERN ; Ecole Polytechnique, Lausanne) et al.
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. [...]
SISSA, 2020 - 11 p. - Published in : PoS AISIS2019 (2020) 044 Fulltext: PDF;
In : Artificial Intelligence for Science, Industry and Society (AISIS 2019), Mexico City, Mexico, 21 - 25 Oct 2019, pp.044

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10.

Automatic Beam Loss Threshold Selection for LHC Collimator Alignment / Azzopardi, Gabriella (CERN) ; Muscat, Adrian (Malta U.) ; Redaelli, Stefano (CERN) ; Salvachua, Belen (CERN) ; Valentino, Gianluca (Malta U.)
The collimation system used in the Large Hadron Collider at CERN is positioned around the beam with a hierarchy that protects sensitive equipment from unavoidable beam losses. The collimator settings are determined using a beam-based alignment technique, where collimator jaws are moved towards the beam until the beam losses exceed a predefined threshold. [...]
2020 - 6 p. - Published in : 10.18429/JACoW-ICALEPCS2019-MOPHA010 Fulltext: PDF;
In : 17th Biennial International Conference on Accelerator and Large Experimental Physics Control Systems (ICALEPCS), New York, United States, 5 - 11 Oct 2019, pp.MOPHA010

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Se også: lignende forfatternavn
88	VALENTINO, Gianluca
70	Valentino, G
16	Valentino, G.

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