Study on the Selection Method of Probability Density Distribution in Nuclear Data Stochastic Sampling

For statistical learning algorithms that are related to various core physical calculations with nuclear data as the analysis input, providing stochastic disturbance samples that are consistent with the known statistical moment information and physical constraints of nuclear data is fundamental. Reasonably perturbed nuclear data samples are one of the important factors to ensure the prediction accuracy of data-driven artificial intelligence models such as core physical response feature extraction and reduced-order modeling. Selecting the probability density distribution that can meet the physical constraints of nuclear data itself is the key to ensure the rationality of the above stochastic sampling of nuclear data. This work focuses on two types of physical constraints that are commonly seen in the evaluated nuclear data library, namely, non-negativity constraints (e.g. fission product yield, nuclear reaction cross section) and normalization constraints (e.g. decay branch ratio), studies the corresponding probability density distribution selection methods and provides the corresponding sampling algorithms. Combined with the uncertainty information of nuclear data provided in the evaluated nuclear data library, this work compares the stochastic sampling effects of nuclear data with different probability density distributions, and gives some suggestions on the selection of probability density distributions.