Comparison of the number of history in Monte Carlo Simulation Programs

TL;DR

This study investigates how the number of histories in Monte Carlo simulations affects detector efficiency estimates, recommending at least 10^7 particles for accuracy and discussing computational considerations.

Contribution

It provides a detailed analysis of the impact of the number of histories on efficiency calculations across different simulation codes, offering practical guidelines.

Findings

At least 10^7 particles are needed for <1% uncertainty.

Increasing to 10^8 particles is beneficial if computational resources allow.

Beyond 10^8 particles, additional benefits are negligible.

Abstract

The use of the Monte Carlo technique in a reliable and inexpensive way without the need for a standard radioactive source in determining the detector efficiency is becoming widespread every passing day. It is important to model the detector with the real dimensions for an accurate and precise results for the method. Another parameter as important as detector modeling is the number of histories in the simulation code examined in this study. In this study, the effect of the number of histories on the efficiency was examined in detail using different simulation codes. The results obtained in this work, 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.