Parametric optimization and multi-task learning for pseudo gamma spectroscopy of plastic scintillation detector플라스틱 섬광체 검출기의 의사 감마분광분석을 위한 매개변수 최적화 및 다중작업학습

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Plastic scintillation detectors have been widely used in radiation measurements owing to their low cost and large volume, However, they are difficult to conduct energy calibration and not adequate spectroscopic measurements because of poor energy resolution and absence of full energy peaks in their spectrum. In this thesis, I study parametric optimization and multi-task learning to allow plastic scintillation detectors to spectroscopic capability. In parametric optimization, I address an optimization problem to calibrate measured channel to physical energy and to find energy broadening parameter for simulating response function of the detectors. In multi-task learning, I develop a deep learning model to generate full energy peaks and to predict relative activities of gamma-ray sources from plastic gamma spectra.
Advisors
Cho, Gyuseongresearcher조규성researcher
Description
한국과학기술원 :원자력및양자공학과,
Publisher
한국과학기술원
Issue Date
2021
Identifier
325007
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 원자력및양자공학과, 2021.2,[vi, 80 p. :]

Keywords

Plastic scintillation detectors▼aParametric optimization▼aEnergy calibration▼aResponse function▼aMulti-task learning▼aPseudo gamma spectroscopy; 플라스틱 섬광체▼a매개변수 최적화▼a에너지 교정▼a반응함수▼a다중작업학습▼a의사 감마분광분석

URI
http://hdl.handle.net/10203/309015
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1006539&flag=dissertation
Appears in Collection
NE-Theses_Ph.D.(박사논문)
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