Developments in data assimilation theory allow to adjust integral parameters and cross sections with stochastic sampling. This work investigates how two stochastic methods, MOCABA and BMC, perform relative to a sensitivity-based methodology called GLLS. Stochastic data assimilation can treat integral parameters that behave non-linearly with respect to nuclear data perturbations, which would be an advantage over GLLS. Additionally, BMC is compatible with integral parameters and nuclear data that have non-Gaussian distributions. In this work, MOCABA and BMC are compared to GLLS for a simple test case: JEZEBEL-Pu239 simulated with Serpent2. The three methods show good agreement between the mean values and uncertainties of their posterior calculated values and nuclear data. The observed discrepancies are not statistically significant with a sample size of 10000. BMC posterior calculated values and nuclear data have larger uncertainties than MOCABA's at equivalent sample sizes.
Matthias Finger, Qian Wang, Yiming Li, Varun Sharma, Konstantin Androsov, Jan Steggemann, Xin Chen, Rakesh Chawla, Matteo Galli, Jian Wang, João Miguel das Neves Duarte, Tagir Aushev, Matthias Wolf, Yi Zhang, Tian Cheng, Yixing Chen, Werner Lustermann, Andromachi Tsirou, Alexis Kalogeropoulos, Andrea Rizzi, Ioannis Papadopoulos, Paolo Ronchese, Hua Zhang, Leonardo Cristella, Siyuan Wang, Tao Huang, David Vannerom, Michele Bianco, Sebastiana Gianì, Sun Hee Kim, Davide Di Croce, Kun Shi, Abhisek Datta, Jian Zhao, Federica Legger, Gabriele Grosso, Anna Mascellani, Ji Hyun Kim, Donghyun Kim, Zheng Wang, Sanjeev Kumar, Wei Li, Yong Yang, Ajay Kumar, Ashish Sharma, Georgios Anagnostou, Joao Varela, Csaba Hajdu, Muhammad Ahmad, Ekaterina Kuznetsova, Ioannis Evangelou, Milos Dordevic, Meng Xiao, Sourav Sen, Xiao Wang, Kai Yi, Jing Li, Rajat Gupta, Hui Wang, Seungkyu Ha, Pratyush Das, Anton Petrov, Xin Sun, Valérie Scheurer, Muhammad Ansar Iqbal, Lukas Layer
Victor Panaretos, Yoav Zemel, Valentina Masarotto