Assessment of the suitability of different random number generators for Monte Carlo simulations in gamma-ray spectrometry

The Monte Carlo method has become a valuable numerical laboratory framework in which to simulate complex physical systems. It is based on the generation of pseudo-random number sequences by numerical algorithms called random generators. In this work we assessed the suitability of different well-known random number generators for the simulation of gamma-ray spectrometry systems during efficiency calibrations. The assessment was carried out in two stages. The generators considered (Delphi's linear congruential, mersenne twister, XorShift, multiplier with carry, universal virtual array, and non-periodic logistic map based generator) were first evaluated with different statistical empirical tests, including moments, correlations, uniformity, independence of terms and the DIEHARD battery of tests. In a second step, an application-specific test was conducted by implementing the generators in our Monte Carlo program DETEFF and comparing the results obtained with them. The calculations were performed with two different CPUs, for a typical HpGe detector and a water sample in Marinelli geometry, with gamma-rays between 59 and 1800 keV. For the Non-periodic Logistic Map based generator, dependence of the most significant bits was evident. This explains the bias, in excess of 5%, of the efficiency values obtained with this generator. The results of the application-specific assessment and the statistical performance of the other algorithms studied indicate their suitability for the Monte Carlo simulation of gamma-ray spectrometry systems for efficiency calculations.