Author: Toyoda, A.
Paper Title Page
WEMPL001 An Application of Machine Learning for the Analysis of Temperature Rise on the Production Target in Hadron Experimental Facility at J-PARC 992
WEPHA003   use link to see paper's listing under its alternate paper code  
  • K. Agari, H. Akiyama, Y. Morino, Y. Sato, A. Toyoda
    KEK, Tsukuba, Japan
  Hadron Experimental Facility (HEF) is designed to handle an intense slow-extraction proton beam from the 30 GeV Main Ring (MR) of Japan Proton Accelerator Research Complex (J-PARC). Proton beams of 5·1013 protons per spill during 2 seconds in the 5.2 seconds accelerator operating cycle were extracted from MR to HEF in the 2018 run. In order to evaluate soundness of the target, we have analyzed variation of temperature rise on the production target, which depends on the beam conditions on the target. Predicted temperature rise is calculated from the existing data of the beam intensity, the spill length (duration of the beam extraction) and the beam position on the target, using a linear regression analysis with a machine learning library, Scikit-learn. As a result, the predicted temperature rise on the production target shows good agreement with the measured one. We have also examined whether the present method of the predicted temperature rise from the existing data can be applied to unknown data in the future runs. The present paper reports the status of the measurement system of temperature rise on the target with machine learning in detail.  
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About • paper received ※ 28 September 2019       paper accepted ※ 10 October 2019       issue date ※ 30 August 2020  
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