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Towards automatic setup of 18 MeV electron beamline using machine learning / Velotti, Francesco Maria (CERN) ; Goddard, Brennan (CERN) ; Kain, Verena (CERN) ; Ramjiawan, Rebecca (CERN) ; Della Porta, Giovanni Zevi (CERN) ; Hirlaender, Simon (U. Salzburg (main))
To improve the performance-critical stability and brightness of the electron bunch at injection into the proton-driven plasma wakefield at the AWAKE CERN experiment, automation approaches based on unsupervised Machine Learning (ML) were developed and deployed. Numerical optimisers were tested together with different model-free reinforcement learning agents. [...]
arXiv:2209.03183.- 2023-04-27 - 18 p. - Published in : Mach. Learn. Sci. Tech. 4 (2023) 025016 Fulltext: document - PDF; 2209.03183 - PDF;

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(91)

Triggering Dark Showers with Conditional Dual Auto-Encoders / Anzalone, Luca (Bologna U. ; INFN, Bologna) ; Chhibra, Simranjit Singh (Bologna U. ; CERN ; Queen Mary, U. of London) ; Maier, Benedikt (CERN ; KIT, Karlsruhe) ; Chernyavskaya, Nadezda (CERN) ; Pierini, Maurizio (CERN)
We present a family of conditional dual auto-encoders (CoDAEs) for generic and model-independent new physics searches at colliders. New physics signals, which arise from new types of particles and interactions, are considered in our study as anomalies causing deviations in data with respect to expected background events. [...]
arXiv:2306.12955.- 2024-09-02 - 24 p. - Published in : Mach. Learn. Sci. Tech. 5 (2024) 035064 Fulltext: 2306.12955 - PDF; document - PDF;

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(82)

Automated Intensity Optimisation Using Reinforcement Learning at LEIR / Madysa, Nico (CERN) ; Alemany-Fernández, Reyes (CERN) ; Biancacci, Nicolo (CERN) ; Goddard, Brennan (CERN) ; Kain, Verena (CERN) ; Velotti, Francesco (CERN)
High intensities in the CERN Low Energy Ion Ring (LEIR) are achieved by stacking up to seven consecutive multi-turn injections from Linac3. Two inclined septa combined with a collapsing horizontal orbit bump allow a 6-D phase space painting via a linearly ramped mean momentum along the Linac3 pulse and injection at high dispersion. [...]
2022 - 4 p. - Published in : JACoW IPAC 2022 (2022) 941-944 Fulltext: PDF;
In : 13th International Particle Accelerator Conference (IPAC 2022), Bangkok, Thailand, 12 - 17 Jun 2022, pp.941-944

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(62)

Conditional Progressive Generative Adversarial Network for satellite image generation / Cardoso, Renato (CERN) ; Vallecorsa, Sofia (Outer Space Office, United Nations) ; Nemni, Edoardo
Image generation and image completion are rapidly evolving fields, thanks to machine learning algorithms that are able to realistically replace missing pixels. [...]
arXiv:2211.15303. - 8 p.
Fulltext

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(59)

Deep generative models for fast shower simulation in ATLAS / Ghosh, Aishik (LAL, Univ. Paris-Sud, IN2P3/CNRS, Universite Paris-Saclay)
The need for large scale and high fidelity simulated samples for the ATLAS experiment motivates the development of new simulation techniques. [...]
ATL-SOFT-PROC-2019-007. - 2020. - 5 p.
Original Communication (restricted to ATLAS) - Full text

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(59)

Hierarchical Reinforcement Learning to control the AWAKE Electron Beamline / Rodriguez Mateos, Borja
New generation particle accelerators are increasingly more complex and have more stringent re- quirements on parameter quality and efficiency [...]
CERN-THESIS-2023-333 - 69.

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(54)

Fully upgraded $\beta$-NMR setup at ISOLDE for high-precision high-field studies / Jankowski, M. (CERN ; Darmstadt, Tech. U.) ; Azaryana, N. (Mickiewicz U., Poznan) ; Baranowski, M. (CERN ; Mickiewicz U., Poznan) ; Bissell, M.L. (CERN ; Manchester U.) ; Brand, H. (Darmstadt, GSI) ; Chojnacki, M. (CERN ; Geneva U.) ; Croese, J. (CERN ; Geneva U. ; TNO/TPD Space Instrum.) ; Dziubinska-Kühn, K.M. (CERN ; Leipzig U. ; Maastricht U.) ; Karg, B. (Geneva U.) ; Madurga Flores, M. (Tennessee U.) et al.
$\beta$-NMR is an advancing technique that enables measurements relevant to various fields of research, ranging from physics to chemistry and biology. [...]
arXiv:2410.00186. - 15.
Fulltext

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(52)

Reinforcement learning and its applications at CERN / Bunino, Matteo (speaker) (CERN)
Abstract:Reinforcement Learning (RL) has emerged as a powerful paradigm in artificial intelligence and has found exciting applications in various fields, including particle accelerators at CERN. This introductory lecture aims to provide an overview of RL and its application in optimizing beam steering in the AWAKE beamline and automating bunch splitting in the Proton Synchrotron (PS) at CERN.The lecture will begin with an introduction to the fundamentals of RL, where we will explore the concept of agents learning to make decisions through interaction with an environment. [...]
2023 - 5763. CERN openlab summer student lecture programme External link: Event details In : Reinforcement learning and its applications at CERN

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(52)

Reinforcement learning and its applications at CERN / Bunino, Matteo (speaker) (CERN)
Abstract:Reinforcement Learning (RL) has emerged as a powerful paradigm in artificial intelligence and has found exciting applications in various fields, including particle accelerators at CERN. This introductory lecture aims to provide an overview of RL and its application in optimizing beam steering in the AWAKE beamline and automating bunch splitting in the Proton Synchrotron (PS) at CERN.The lecture will begin with an introduction to the fundamentals of RL, where we will explore the concept of agents learning to make decisions through interaction with an environment. [...]
2024 - 7201. CERN openlab summer student lecture programme External link: Event details In : Reinforcement learning and its applications at CERN

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(52)

Reinforcement learning and its applications at CERN / Bunino, Matteo (speaker) (CERN)
Abstract:Reinforcement Learning (RL) has emerged as a powerful paradigm in artificial intelligence and has found exciting applications in various fields, including particle accelerators at CERN. This introductory lecture aims to provide an overview of RL and its application in optimizing beam steering in the AWAKE beamline and automating bunch splitting in the Proton Synchrotron (PS) at CERN.The lecture will begin with an introduction to the fundamentals of RL, where we will explore the concept of agents learning to make decisions through interaction with an environment. [...]
2024 - 7668. CERN openlab summer student lecture programme External link: Event details In : Reinforcement learning and its applications at CERN

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