RadStat: An open-source statistical analysis tool for counts obtained by a GM counter

Mehrdad Shahmohammadi Beni; Hiroshi Watabe; Wing Sum Kwan; M Rafiqul Islam; Kwan Ngok Yu

doi:10.1371/journal.pone.0267610

RadStat: An open-source statistical analysis tool for counts obtained by a GM counter

PLoS One. 2022 May 31;17(5):e0267610. doi: 10.1371/journal.pone.0267610. eCollection 2022.

Authors

Mehrdad Shahmohammadi Beni^{1

2}, Hiroshi Watabe^{2

3}, Wing Sum Kwan¹, M Rafiqul Islam³, Kwan Ngok Yu¹

Affiliations

¹ Department of Physics, City University of Hong Kong, Hong Kong, China.
² Division of Radiation Protection and Safety Control, Cyclotron and Radioisotope Center, Tohoku University, Sendai, Japan.
³ Graduate School of Biomedical Engineering, Tohoku University, Sendai, Japan.

Abstract

The interaction of ionizing radiation with matter is a stochastic process and statistical analysis of such a process would be a crucial step in understanding radioactivity. Geiger-Müller (GM) counter is a widely used radiation detector used in nuclear radiation surveying, which produces counts upon exposure to a radioactive source. There are a variety of multi-purpose software that can be used to perform statistical analysis of measured counts from a GM counter. However, statistical analysis is a lengthy, error prone and time-consuming process, which gets more tedious when the number of measurements increases. In the present work, we have developed an open-source and easy-to-use graphical user interface (GUI) computer program named RadStat for statistical analysis of counts measured by a GM counter. RadStat has its own scripting syntaxes and bundled with gnuplot for quick visualization of output results. We believe the present open-source GUI program would be a useful tool for research and teaching of nuclear radiation physics.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Radiometry*
Research Design
Software*
Stochastic Processes

Associated data

figshare/10.6084/m9.figshare.17876057.v1

Grants and funding

The present work was supported by the Special Grant for the Development of Virtual Teaching and Learning (VTL) no. 6430120 from the University Grants Committee of Hong Kong SAR (https://www.ugc.edu.hk/eng/ugc/about/press_speech_other/press/2021/pr20210121.html) (awarded to K.N. Yu). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.