Implementation of calculation codes in gamma spectrometry measurements for corrections of systematic effects
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Four calculation codes were implemented on experimental data to calculate corrections of common systematic effects within high resolution gamma spectrometry. The detector parameters were sparsely optimized to imitate the usage that can be expected from personnel with limited experience. The transfer of the efficiency from the calibration geometry (60 ml, density 1.0 g/cm3) to 200 ml geometry (density 1.5 g/cm3) failed with all codes, which was discouraging. However, a majority of the other corrected activities deviated with less than 10 % from the reference activity values, even for density corrections or when corrections had been calculated for new source-detector-geometries. Smaller deviations, around 5 % or less, were achieved when corrections were done within the calibrated geometry. This shows a robustness of the calculation codes even if the in-data is not perfectly optimized.
KeywordsGamma-ray spectrometry Coincidence summing Efficiency transfer Simulation Systematic effects
The Swedish Ministry of defence is greatly acknowledged for funding this work.
- 1.Andreev DS, Erokhina KI, Zvonov VS, Lemberg IKh (1973) Determination of ɣ-detection efficiency in energy peaks by means of nuclides having complicated decay scheme in close-geometry conditions. Izv Akad Nauk SSSR Ser Fiz 37(8):1609–1612Google Scholar
- 14.Lépy M-C, Altzitzoglou T, Anagnostakis MJ, Arnold D, Capogni M, Ceccatelli A, De Felice P, Dersch R, Dryak P, Fazio A, Ferreux L, Guardati M, Han J, B, Hurtado S, Karfopoulos KL, Klemola S, Kovar P, Laubenstein M, Lee KB, Ocone R, Ott O, Sima O, Sudar S, Švec A, Chau Van Tao, Tran Thien Thanh, Vidmar T (2010) Intercomparison of methods for coincidence summing corrections in gamma-ray spectrometry. Appl Radiat Isot 68:1407–1412Google Scholar
- 17.Plenteda R (2002) A Monte Carlo based virtual gamma spectroscopy laboratory. Ph.D. Thesis. Universitätsbibliothek der Technischen Universität WienGoogle Scholar
- 22.Lépy MC, Altzitzoglou T, Arnold D, Bronson F, Capote Noy R, Décombaz M, De Corte F, Edelmaier R, Herrera Peraza E, Klemola S, Korun M, Kralik M, Neder H, Plagnard J, Pommé S, de Sanoit J, Sima O, Ugletveit F, Van Velzen L, Vidmar T (2001) Intercomparison of efficiency transfer software for gamma-ray spectrometry. Appl Radiat Isot 55:493–503CrossRefGoogle Scholar
- 25.Kessel R, Berglund M, Taylor PDP, Wellum R (2001) How to treat correlations in the uncertainty budget, when combining results from different measurements. S Adv Math 57:231–241Google Scholar
- 28.Vidmar T, Aubineau-Laniece I, Anagnostakis MJD, Arnold Brettner-Messler R, Budjas D, Capogni M, Dias MS, De Geer L-E, Fazio A, Gasparro J, Hult M, Hurtado S, Vargas MJ, Laubenstein M, Lee KB, Lee Y-K, Lépy M-C, Maringer F-J, Medina Peyres V, Mille M, Moralles M, Nour S, Plenteda R, Rubio Montero MP, Sima O, Tomei C, Vidmar G (2008) An intercomparison of Monte Carlo codes used in gamma-ray spectrometry. Appl Radiat Isot 66:764–768CrossRefGoogle Scholar