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A Novel Method for Detecting Unidentified Falling Object Loss Patterns in the LHC
/ Coyle, Loic (CERN) ; Blanc, Frederic (Ecole Polytechnique, Lausanne) ; Di Croce, Davide (Ecole Polytechnique, Lausanne) ; Lechner, Anton (CERN) ; Mirarchi, Daniele (CERN) ; Pieloni, Tatiana (Ecole Polytechnique, Lausanne) ; Solfaroli Camillocci, Matteo (CERN) ; Wenninger, Jorg (CERN)
Understanding and mitigating particle losses in the Large Hadron Collider (LHC) is essential for both machine safety and efficient operation. Abnormal loss distributions are telltale signs of abnormal beam behaviour or incorrect machine configuration. [...]
2022 - 4 p.
- Published in : JACoW IPAC 2022 (2022) 953-956
Fulltext: PDF;
In : 13th International Particle Accelerator Conference (IPAC 2022), Bangkok, Thailand, 12 - 17 Jun 2022, pp.953-956
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2.
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Data-driven modeling of beam loss in the LHC
/ Krymova, Ekaterina (Ecole Polytechnique, Lausanne ; Zurich, ETH) ; Obozinski, Guillaume (Ecole Polytechnique, Lausanne ; Zurich, ETH) ; Schenk, Michael (CERN) ; Coyle, Loic (Ecole Polytechnique, Lausanne ; CERN) ; Pieloni, Tatiana (Ecole Polytechnique, Lausanne ; CERN)
In the Large Hadron Collider, the beam losses are continuously measured for machine protection. By design, most of the particle losses occur in the collimation system, where the particles with high oscillation amplitudes or large momentum error are scraped from the beams. [...]
arXiv:2208.08935.-
2023-01-05 - 16 p.
- Published in : Front. Phys. 10 (2023) 960963
Fulltext: 2208.08935 - PDF; Publication - PDF;
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3.
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Modeling Particle Stability Plots for Accelerator Optimization Using Adaptive Sampling
/ Schenk, Michael (Ecole Polytechnique, Lausanne ; CERN) ; Coyle, Loic (CERN ; Ecole Polytechnique, Lausanne) ; Giovannozzi, Massimo (CERN) ; Krymova, Ekaterina (Ecole Polytechnique, Lausanne ; Zurich, ETH) ; Mereghetti, Alessio (CERN ; CNAO, Milan) ; Obozinski, Guillaume (Ecole Polytechnique, Lausanne ; Zurich, ETH) ; Pieloni, Tatiana (CERN ; Ecole Polytechnique, Lausanne)
One key aspect of accelerator optimization is to maximize the dynamic aperture (DA) of a ring. Given the number of adjustable parameters and the compute-intensity of DA simulations, this task can benefit significantly from efficient search algorithms of the available parameter space. [...]
Geneva : JACoW, 2021 - 4 p.
- Published in : JACoW IPAC 2021 (2021) 1923-1926
Fulltext: PDF;
In : 12th International Particle Accelerator Conference (IPAC 2021), Online, 24 - 28 May 2021, pp.1923-1926
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4.
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Detection and Classification of Collective Beam Behaviour in the LHC
/ Coyle, Loic (Ecole Polytechnique, Lausanne ; CERN) ; Blanc, Frederic (Ecole Polytechnique, Lausanne) ; Buffat, Xavier (CERN) ; Krymova, Ekaterina (Ecole Polytechnique, Lausanne ; Zurich, ETH) ; Obozinski, Guillaume (Ecole Polytechnique, Lausanne ; Zurich, ETH) ; Pieloni, Tatiana (Ecole Polytechnique, Lausanne ; CERN) ; Schenk, Michael (Ecole Polytechnique, Lausanne ; CERN) ; Solfaroli Camillocci, Matteo (CERN) ; Wenninger, Jorg (CERN)
Collective instabilities can lead to a severe deterioration of beam quality, in terms of reduced beam intensity and increased beam emittance, and consequently a reduction of the collider’s luminosity. It is therefore crucial for the operation of the CERN’s Large Hadron Collider to understand the conditions in which they appear in order to find appropriate mitigation measures. [...]
Geneva : JACoW, 2021 - 4 p.
- Published in : JACoW IPAC 2021 (2021) 4318-4321
Fulltext: PDF;
In : 12th International Particle Accelerator Conference (IPAC 2021), Online, 24 - 28 May 2021, pp.4318-4321
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5.
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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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6.
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Beam Measurements and Machine Learning at the CERN Large Hadron Collider
/ Arpaia, Pasquale (Naples U.) ; Azzopardi, Gabriella (CERN) ; Blanc, Frédéric (LPHE, Lausanne) ; Buffat, Xavier (CERN) ; Coyle, Loic (CERN ; LPHE, Lausanne) ; Fol, Elena (CERN ; Frankfurt U.) ; Giordano, Francesco (Naples U. ; CERN) ; Giovannozzi, Massimo (CERN) ; Pieloni, Tatiana (LPHE, Lausanne) ; Prevete, Roberto (Naples U.) et al.
This paper presents a review of the recent Machine Learning activities carried out on beam measurements performed at the CERN Large Hadron Collider. This paper has been accepted for publication in IEEE Instrumentation and Measurement Magazine and in the published version no abstract is provided..
arXiv:2107.12641.-
2021-11-18 - 26 p.
- Published in : IEEE Instrum. Measur. Mag. 24 (2021) 47-58
Fulltext: PDF;
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7.
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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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