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Preliminary study: Development of an automated data collection system for gamma ray measurements using Python

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Publicat a:IOP Conference Series. Materials Science and Engineering vol. 1326, no. 1 (Apr 2025), p. 012002
Autor principal: Yusuff, SM
Altres autors: Tamron, MANM, Mustapha, I, Ismail, AH, MF Abdul Rahim, Idris, SA, NA Abd Rahman, Ramli, N
Publicat:
IOP Publishing
Matèries:
Radioactive materials
Cloud computing
Radiation effects
Gamma rays
Data collection
Python
Graphical user interface
Visual programming languages
Automation
Radiation
Coding
Accés en línia:Citation/Abstract
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Resum:This paper presents an automated data collection system for gammaray measurement using the Python programming language. The system automates experimental data collection from gamma-ray detection systems, providing greater confidence in research results. The objective of this study is to develop a Python coding system for automated data collection and to produce significant graphical data results. The study used Python coding to continuously measure and analyze gamma rays emitted from Ba-133 radioactive material, which were detected using a scintillation detector and counted using a scaler ratemeter. The coding system used NumPy, Pandas, Matplotlib, and Tkinter Python packages, and Microsoft Visual Studio Community for integration. The Beer-Lambert attenuation law formula is embedded in the coding system to produce intensity against time graphical data results for various sample materials. The developed Python code was integrated into the gamma-ray detection system. The automated data collection system operates without significant lag or errors for 3 hours of gamma-ray measurements with 6 to 10 seconds of time interval speed. The graphical user interface (GUI) and data output are visualized immediately and continuously, and all data output can be safely stored on the computer. A Python-based automated data collection system was successfully developed, allowing efficient visualization of significant graphical data results. This system can reduce radiation exposure for radiation workers and may integrate with cloud computing for remote operation in the future.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/1326/1/012002
Font:Materials Science Database