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TY - CONF AU - Agari, K. AU - Akiyama, H. AU - Morino, Y. AU - Sato, Y. AU - Toyoda, A. ED - White, Karen S. ED - Brown, Kevin A. ED - Dyer, Philip S. ED - Schaa, Volker RW TI - An Application of Machine Learning for the Analysis of Temperature Rise on the Production Target in Hadron Experimental Facility at J-PARC J2 - Proc. of ICALEPCS2019, New York, NY, USA, 05-11 October 2019 CY - New York, NY, USA T2 - International Conference on Accelerator and Large Experimental Physics Control Systems T3 - 17 LA - english AB - 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. PB - JACoW Publishing CP - Geneva, Switzerland SP - 992 EP - 996 KW - target KW - operation KW - proton KW - EPICS KW - extraction DA - 2020/08 PY - 2020 SN - 2226-0358 SN - 978-3-95450-209-7 DO - doi:10.18429/JACoW-ICALEPCS2019-WEMPL001 UR - https://jacow.org/icalepcs2019/papers/wempl001.pdf ER -