Studies of Big Data Processing at Linear Accelerator Sources Using Machine Learning
| Autor: | Mohammed Bawatna, Bertram Green |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
010308 nuclear & particles physics
Computer science business.industry Terahertz radiation Detector Big data Cloud computing Machine learning computer.software_genre 01 natural sciences Linear particle accelerator Power (physics) 0103 physical sciences Cathode ray Physics::Accelerator Physics Artificial intelligence business 010303 astronomy & astrophysics computer Beam (structure) |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9783030519704 CSOC (2) |
| DOI: | 10.1007/978-3-030-51971-1_37 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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