Comparison of the Number of Particle History for Monte Carlo Codes in Gamma-Ray Spectroscopy

Abstract

Monte Carlo is a numerical computation algorithm that is widely used in many fields of science and is used to obtain numerical results with a large number of repeated random samplings. Radiation transport with Monte Carlo simulation continues to increase its popularity in the fields of radiation measurement. The high accuracy and precision measurement of radionuclide activity amounts in gamma-ray spectrometry depends on the efficiency calibration of the detector. Efficiency calibration is carried out in two ways, using certified reference materials, by experimental method or Monte Carlo simulation method. The experimental method is expensive, procedurally complex and time-consuming due to the supply of reference material. The use of the Monte Carlo technique in a reliable way without the need for a standard radioactive source in determining the detector efficiency is becoming common. The most critical step for accurate and precise results in getting the response of a detector with the Monte Carlo method is modeling the detector with its realistic dimensions. Another parameter as important as detector modeling is the number of histories in the simulation code examined in this study. The effect of the number of histories on efficiency was examined in detail using PHITS, GESPECOR and DETEFF Monte Carlo simulation codes. Since there is no definite number about this effect, which is important for obtaining meaningful and realistic results, the change in the efficiency value was examined by increasing the number of stories from 105 to 108. The results obtained in this work showed that at least 107 particle numbers should be used in all three programs where the uncertainty is below 1%. If the existing facilities are sufficient, it can be increased to 108s in case of having a more equipped and fast computer. However, going higher than this value does not make any sense as seen from the study.