Machine learning for beam dynamics studies at the CERN Large Hadron Collider
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. It is only up until recently that Machine Learning has started to be...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Arpaia, P. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: The efficacy of EEG-biofeedback for acute pain management, a randomized sham-controlled study of a tailored protocol - Ide, C.V. ELSEVIER, 2017, a journal on accelerators, instrumentation and techniques applied to research in nuclear and atomic physics, materials science and related fields in physics, Amsterdam |
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| Übergeordnetes Werk: |
volume:985 ; year:2021 ; day:1 ; month:01 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.nima.2020.164652 |
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| 520 | |a 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. It is only up until recently that Machine Learning has started to be applied successfully in the domain of Accelerator Physics, which is testified by intense efforts deployed in this domain by several laboratories worldwide. This is also the case of CERN, where recently focused efforts have been devoted to the application of Machine Learning techniques to beam dynamics studies at the Large Hadron Collider (LHC). This implies a wide spectrum of applications from beam measurements and machine performance optimisation to analysis of numerical data from tracking simulations of non-linear beam dynamics. In this paper, the LHC-related applications that are currently pursued are presented and discussed in detail, paying also attention to future developments. | ||
| 520 | |a 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. It is only up until recently that Machine Learning has started to be applied successfully in the domain of Accelerator Physics, which is testified by intense efforts deployed in this domain by several laboratories worldwide. This is also the case of CERN, where recently focused efforts have been devoted to the application of Machine Learning techniques to beam dynamics studies at the Large Hadron Collider (LHC). This implies a wide spectrum of applications from beam measurements and machine performance optimisation to analysis of numerical data from tracking simulations of non-linear beam dynamics. In this paper, the LHC-related applications that are currently pursued are presented and discussed in detail, paying also attention to future developments. | ||
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| 700 | 1 | |a Redaelli, S. |4 oth | |
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10.1016/j.nima.2020.164652 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001261.pica (DE-627)ELV05207238X (ELSEVIER)S0168-9002(20)31049-4 DE-627 ger DE-627 rakwb eng 610 VZ 44.90 bkl Arpaia, P. verfasserin aut Machine learning for beam dynamics studies at the CERN Large Hadron Collider 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. It is only up until recently that Machine Learning has started to be applied successfully in the domain of Accelerator Physics, which is testified by intense efforts deployed in this domain by several laboratories worldwide. This is also the case of CERN, where recently focused efforts have been devoted to the application of Machine Learning techniques to beam dynamics studies at the Large Hadron Collider (LHC). This implies a wide spectrum of applications from beam measurements and machine performance optimisation to analysis of numerical data from tracking simulations of non-linear beam dynamics. In this paper, the LHC-related applications that are currently pursued are presented and discussed in detail, paying also attention to future developments. 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. It is only up until recently that Machine Learning has started to be applied successfully in the domain of Accelerator Physics, which is testified by intense efforts deployed in this domain by several laboratories worldwide. This is also the case of CERN, where recently focused efforts have been devoted to the application of Machine Learning techniques to beam dynamics studies at the Large Hadron Collider (LHC). This implies a wide spectrum of applications from beam measurements and machine performance optimisation to analysis of numerical data from tracking simulations of non-linear beam dynamics. In this paper, the LHC-related applications that are currently pursued are presented and discussed in detail, paying also attention to future developments. LHC Elsevier Beam dynamics Elsevier Machine Learning Elsevier Azzopardi, G. oth Blanc, F. oth Bregliozzi, G. oth Buffat, X. oth Coyle, L. oth Fol, E. oth Giordano, F. oth Giovannozzi, M. oth Pieloni, T. oth Prevete, R. oth Redaelli, S. oth Salvachua, B. oth Salvant, B. oth Schenk, M. oth Camillocci, M. Solfaroli oth Tomás, R. oth Valentino, G. oth Van der Veken, F.F. oth Wenninger, J. oth Enthalten in North-Holland Publ. Co Ide, C.V. ELSEVIER The efficacy of EEG-biofeedback for acute pain management, a randomized sham-controlled study of a tailored protocol 2017 a journal on accelerators, instrumentation and techniques applied to research in nuclear and atomic physics, materials science and related fields in physics Amsterdam (DE-627)ELV000874671 volume:985 year:2021 day:1 month:01 pages:0 https://doi.org/10.1016/j.nima.2020.164652 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ AR 985 2021 1 0101 0 |
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10.1016/j.nima.2020.164652 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001261.pica (DE-627)ELV05207238X (ELSEVIER)S0168-9002(20)31049-4 DE-627 ger DE-627 rakwb eng 610 VZ 44.90 bkl Arpaia, P. verfasserin aut Machine learning for beam dynamics studies at the CERN Large Hadron Collider 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. It is only up until recently that Machine Learning has started to be applied successfully in the domain of Accelerator Physics, which is testified by intense efforts deployed in this domain by several laboratories worldwide. This is also the case of CERN, where recently focused efforts have been devoted to the application of Machine Learning techniques to beam dynamics studies at the Large Hadron Collider (LHC). This implies a wide spectrum of applications from beam measurements and machine performance optimisation to analysis of numerical data from tracking simulations of non-linear beam dynamics. In this paper, the LHC-related applications that are currently pursued are presented and discussed in detail, paying also attention to future developments. 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. It is only up until recently that Machine Learning has started to be applied successfully in the domain of Accelerator Physics, which is testified by intense efforts deployed in this domain by several laboratories worldwide. This is also the case of CERN, where recently focused efforts have been devoted to the application of Machine Learning techniques to beam dynamics studies at the Large Hadron Collider (LHC). This implies a wide spectrum of applications from beam measurements and machine performance optimisation to analysis of numerical data from tracking simulations of non-linear beam dynamics. In this paper, the LHC-related applications that are currently pursued are presented and discussed in detail, paying also attention to future developments. LHC Elsevier Beam dynamics Elsevier Machine Learning Elsevier Azzopardi, G. oth Blanc, F. oth Bregliozzi, G. oth Buffat, X. oth Coyle, L. oth Fol, E. oth Giordano, F. oth Giovannozzi, M. oth Pieloni, T. oth Prevete, R. oth Redaelli, S. oth Salvachua, B. oth Salvant, B. oth Schenk, M. oth Camillocci, M. Solfaroli oth Tomás, R. oth Valentino, G. oth Van der Veken, F.F. oth Wenninger, J. oth Enthalten in North-Holland Publ. Co Ide, C.V. ELSEVIER The efficacy of EEG-biofeedback for acute pain management, a randomized sham-controlled study of a tailored protocol 2017 a journal on accelerators, instrumentation and techniques applied to research in nuclear and atomic physics, materials science and related fields in physics Amsterdam (DE-627)ELV000874671 volume:985 year:2021 day:1 month:01 pages:0 https://doi.org/10.1016/j.nima.2020.164652 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ AR 985 2021 1 0101 0 |
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10.1016/j.nima.2020.164652 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001261.pica (DE-627)ELV05207238X (ELSEVIER)S0168-9002(20)31049-4 DE-627 ger DE-627 rakwb eng 610 VZ 44.90 bkl Arpaia, P. verfasserin aut Machine learning for beam dynamics studies at the CERN Large Hadron Collider 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. It is only up until recently that Machine Learning has started to be applied successfully in the domain of Accelerator Physics, which is testified by intense efforts deployed in this domain by several laboratories worldwide. This is also the case of CERN, where recently focused efforts have been devoted to the application of Machine Learning techniques to beam dynamics studies at the Large Hadron Collider (LHC). This implies a wide spectrum of applications from beam measurements and machine performance optimisation to analysis of numerical data from tracking simulations of non-linear beam dynamics. In this paper, the LHC-related applications that are currently pursued are presented and discussed in detail, paying also attention to future developments. 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. It is only up until recently that Machine Learning has started to be applied successfully in the domain of Accelerator Physics, which is testified by intense efforts deployed in this domain by several laboratories worldwide. This is also the case of CERN, where recently focused efforts have been devoted to the application of Machine Learning techniques to beam dynamics studies at the Large Hadron Collider (LHC). This implies a wide spectrum of applications from beam measurements and machine performance optimisation to analysis of numerical data from tracking simulations of non-linear beam dynamics. In this paper, the LHC-related applications that are currently pursued are presented and discussed in detail, paying also attention to future developments. LHC Elsevier Beam dynamics Elsevier Machine Learning Elsevier Azzopardi, G. oth Blanc, F. oth Bregliozzi, G. oth Buffat, X. oth Coyle, L. oth Fol, E. oth Giordano, F. oth Giovannozzi, M. oth Pieloni, T. oth Prevete, R. oth Redaelli, S. oth Salvachua, B. oth Salvant, B. oth Schenk, M. oth Camillocci, M. Solfaroli oth Tomás, R. oth Valentino, G. oth Van der Veken, F.F. oth Wenninger, J. oth Enthalten in North-Holland Publ. Co Ide, C.V. ELSEVIER The efficacy of EEG-biofeedback for acute pain management, a randomized sham-controlled study of a tailored protocol 2017 a journal on accelerators, instrumentation and techniques applied to research in nuclear and atomic physics, materials science and related fields in physics Amsterdam (DE-627)ELV000874671 volume:985 year:2021 day:1 month:01 pages:0 https://doi.org/10.1016/j.nima.2020.164652 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ AR 985 2021 1 0101 0 |
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10.1016/j.nima.2020.164652 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001261.pica (DE-627)ELV05207238X (ELSEVIER)S0168-9002(20)31049-4 DE-627 ger DE-627 rakwb eng 610 VZ 44.90 bkl Arpaia, P. verfasserin aut Machine learning for beam dynamics studies at the CERN Large Hadron Collider 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. It is only up until recently that Machine Learning has started to be applied successfully in the domain of Accelerator Physics, which is testified by intense efforts deployed in this domain by several laboratories worldwide. This is also the case of CERN, where recently focused efforts have been devoted to the application of Machine Learning techniques to beam dynamics studies at the Large Hadron Collider (LHC). This implies a wide spectrum of applications from beam measurements and machine performance optimisation to analysis of numerical data from tracking simulations of non-linear beam dynamics. In this paper, the LHC-related applications that are currently pursued are presented and discussed in detail, paying also attention to future developments. 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. It is only up until recently that Machine Learning has started to be applied successfully in the domain of Accelerator Physics, which is testified by intense efforts deployed in this domain by several laboratories worldwide. This is also the case of CERN, where recently focused efforts have been devoted to the application of Machine Learning techniques to beam dynamics studies at the Large Hadron Collider (LHC). This implies a wide spectrum of applications from beam measurements and machine performance optimisation to analysis of numerical data from tracking simulations of non-linear beam dynamics. In this paper, the LHC-related applications that are currently pursued are presented and discussed in detail, paying also attention to future developments. LHC Elsevier Beam dynamics Elsevier Machine Learning Elsevier Azzopardi, G. oth Blanc, F. oth Bregliozzi, G. oth Buffat, X. oth Coyle, L. oth Fol, E. oth Giordano, F. oth Giovannozzi, M. oth Pieloni, T. oth Prevete, R. oth Redaelli, S. oth Salvachua, B. oth Salvant, B. oth Schenk, M. oth Camillocci, M. Solfaroli oth Tomás, R. oth Valentino, G. oth Van der Veken, F.F. oth Wenninger, J. oth Enthalten in North-Holland Publ. Co Ide, C.V. ELSEVIER The efficacy of EEG-biofeedback for acute pain management, a randomized sham-controlled study of a tailored protocol 2017 a journal on accelerators, instrumentation and techniques applied to research in nuclear and atomic physics, materials science and related fields in physics Amsterdam (DE-627)ELV000874671 volume:985 year:2021 day:1 month:01 pages:0 https://doi.org/10.1016/j.nima.2020.164652 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ AR 985 2021 1 0101 0 |
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10.1016/j.nima.2020.164652 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001261.pica (DE-627)ELV05207238X (ELSEVIER)S0168-9002(20)31049-4 DE-627 ger DE-627 rakwb eng 610 VZ 44.90 bkl Arpaia, P. verfasserin aut Machine learning for beam dynamics studies at the CERN Large Hadron Collider 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. It is only up until recently that Machine Learning has started to be applied successfully in the domain of Accelerator Physics, which is testified by intense efforts deployed in this domain by several laboratories worldwide. This is also the case of CERN, where recently focused efforts have been devoted to the application of Machine Learning techniques to beam dynamics studies at the Large Hadron Collider (LHC). This implies a wide spectrum of applications from beam measurements and machine performance optimisation to analysis of numerical data from tracking simulations of non-linear beam dynamics. In this paper, the LHC-related applications that are currently pursued are presented and discussed in detail, paying also attention to future developments. 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. It is only up until recently that Machine Learning has started to be applied successfully in the domain of Accelerator Physics, which is testified by intense efforts deployed in this domain by several laboratories worldwide. This is also the case of CERN, where recently focused efforts have been devoted to the application of Machine Learning techniques to beam dynamics studies at the Large Hadron Collider (LHC). This implies a wide spectrum of applications from beam measurements and machine performance optimisation to analysis of numerical data from tracking simulations of non-linear beam dynamics. In this paper, the LHC-related applications that are currently pursued are presented and discussed in detail, paying also attention to future developments. LHC Elsevier Beam dynamics Elsevier Machine Learning Elsevier Azzopardi, G. oth Blanc, F. oth Bregliozzi, G. oth Buffat, X. oth Coyle, L. oth Fol, E. oth Giordano, F. oth Giovannozzi, M. oth Pieloni, T. oth Prevete, R. oth Redaelli, S. oth Salvachua, B. oth Salvant, B. oth Schenk, M. oth Camillocci, M. Solfaroli oth Tomás, R. oth Valentino, G. oth Van der Veken, F.F. oth Wenninger, J. oth Enthalten in North-Holland Publ. Co Ide, C.V. ELSEVIER The efficacy of EEG-biofeedback for acute pain management, a randomized sham-controlled study of a tailored protocol 2017 a journal on accelerators, instrumentation and techniques applied to research in nuclear and atomic physics, materials science and related fields in physics Amsterdam (DE-627)ELV000874671 volume:985 year:2021 day:1 month:01 pages:0 https://doi.org/10.1016/j.nima.2020.164652 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ AR 985 2021 1 0101 0 |
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Enthalten in The efficacy of EEG-biofeedback for acute pain management, a randomized sham-controlled study of a tailored protocol Amsterdam volume:985 year:2021 day:1 month:01 pages:0 |
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Enthalten in The efficacy of EEG-biofeedback for acute pain management, a randomized sham-controlled study of a tailored protocol Amsterdam volume:985 year:2021 day:1 month:01 pages:0 |
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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. It is only up until recently that Machine Learning has started to be applied successfully in the domain of Accelerator Physics, which is testified by intense efforts deployed in this domain by several laboratories worldwide. This is also the case of CERN, where recently focused efforts have been devoted to the application of Machine Learning techniques to beam dynamics studies at the Large Hadron Collider (LHC). This implies a wide spectrum of applications from beam measurements and machine performance optimisation to analysis of numerical data from tracking simulations of non-linear beam dynamics. In this paper, the LHC-related applications that are currently pursued are presented and discussed in detail, paying also attention to future developments. |
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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. It is only up until recently that Machine Learning has started to be applied successfully in the domain of Accelerator Physics, which is testified by intense efforts deployed in this domain by several laboratories worldwide. This is also the case of CERN, where recently focused efforts have been devoted to the application of Machine Learning techniques to beam dynamics studies at the Large Hadron Collider (LHC). This implies a wide spectrum of applications from beam measurements and machine performance optimisation to analysis of numerical data from tracking simulations of non-linear beam dynamics. In this paper, the LHC-related applications that are currently pursued are presented and discussed in detail, paying also attention to future developments. |
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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. It is only up until recently that Machine Learning has started to be applied successfully in the domain of Accelerator Physics, which is testified by intense efforts deployed in this domain by several laboratories worldwide. This is also the case of CERN, where recently focused efforts have been devoted to the application of Machine Learning techniques to beam dynamics studies at the Large Hadron Collider (LHC). This implies a wide spectrum of applications from beam measurements and machine performance optimisation to analysis of numerical data from tracking simulations of non-linear beam dynamics. In this paper, the LHC-related applications that are currently pursued are presented and discussed in detail, paying also attention to future developments. |
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