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中文摘要：
在反应堆中微子实验的物理数据分析中,前后事例间的时间关联分析是非常重要的一环.对事例间时间结构特性的研究,将有助于理解本底事例产生的物理机制,有利于对信号事例的挑选和对本底事例的排除.与此相应,在产生可用于物理分析的反应堆中微子Monte Carlo（MC）模拟数据的过程中,重构不同事例间的时间关联是一个重要步骤.通过研究大亚湾反应堆中微子实验的数据特点,开发了interleaving算法用于产生带有时间关联的海量物理数据;利用该方法实现了对不同样本的MC数据按事例率的混合;并简要给出了interleaving算法的空间和时间复杂度分析。
英文摘要：
In reactor neutrino experiments, analysis of time correlation between different readout events is an important task. Investigation of the properties of the time structure of events can help to understand the physics mechanism of the backgrounds, as well as to study event selection and background estimation. In the process of generating Monte Carlo （MC） data for physics analysis in reactor experiments,reconstruction of time correlation between different events is apparently an important step. An interleaving algorithm was designed for producing large MC data in the Daya Bay reactor neutrino experiment, which makes it possible to mix different MC data samples according to their event rates. The time and spatial complexity of this algorithm was also briefly analyzed.
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