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Studies of Big Data Processing at Linear Accelerator Sources Using Machine Learning

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Artificial Intelligence and Bioinspired Computational Methods (CSOC 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1225))

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Abstract

In linear accelerator sources such as the electron beam of the super-conducting linear accelerator at the radiation source Electron Linear accelerator for beams with high Brilliance and low Emittance (ELBE), different kinds of secondary radiation can be produced for various research purposes from materials science up to medicine. A variety of different beam detectors generate a huge amount of data, which take a great deal of computing power to capture and analyse. In this contribution, we will discuss the possibilities of using Machine Learning method to solve the big data challenges. Moreover, we will present a technique that employ the machine learning strategy for the diagnostics of high-field terahertz pulses generated at the ELBE accelerator with extremely flexible parameters such as repetition rate, pulse form and polarization.

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Authors and Affiliations

  1. Faculty of Computer Science, Technische Universität, 01069, Dresden, Germany

    Mohammed Bawatna

  2. Institute of Radiation Physics, HZDR, 01328, Dresden, Germany

    Mohammed Bawatna

  3. Magnet Science and Technology, Tallahassee, FL, 32310, USA

    Bertram Green

Authors
  1. Mohammed Bawatna
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  2. Bertram Green
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Correspondence to Mohammed Bawatna .

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Editors and Affiliations

  1. Tomas Bata University, Zlín, Czech Republic

    Radek Silhavy

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Bawatna, M., Green, B. (2020). Studies of Big Data Processing at Linear Accelerator Sources Using Machine Learning. In: Silhavy, R. (eds) Artificial Intelligence and Bioinspired Computational Methods. CSOC 2020. Advances in Intelligent Systems and Computing, vol 1225. Springer, Cham. https://doi.org/10.1007/978-3-030-51971-1_37

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