In this paper a new method of experimental data analysis, the Particle-Set Identification method, is presented. The method allows to reconstruct moments of multiplicity distribution of identified particles. The difficulty the method copes with is due to incomplete particle identification – a particle mass is frequently determined with a resolution which does not allow for a unique determination of the particle type. Within this method the moments of order k are calculated from mean multiplicities of k-particle sets of a given type. The Particle-Set Identification method remains valid even in the case of correlations between mass measurements for different particles. This distinguishes it from the Identity method introduced by us previously to solve the problem of incomplete particle identification in studies of particle fluctuations.

本文提出了一种新的实验数据分析方法，即粒子集识别方法。该方法允许重构已识别颗粒的多重分布矩。该方法解决的困难是由于不完整的颗粒识别-经常以无法唯一确定颗粒类型的分辨率来确定颗粒质量。在这个方法中阶矩ķ从平均值的计算多重ķ-给定类型的粒子集。即使在不同粒子的质量测量之间存在相关性的情况下，粒子集识别方法仍然有效。这与我们先前引入的识别方法的区别在于解决粒子波动研究中粒子识别不完全的问题。