标题： 使用贝叶斯推理对散裂模型进行参数优化 显示英文标题

标题： Parameter optimisation using Bayesian inference for spallation models

摘要： 高能散裂模型的准确性和精确性是设计和开发新应用和实验的关键问题。我们提出了一种方法，通过利用广义最小二乘法的贝叶斯版本来估计模型参数及其相关不确定性，这使我们能够结合关于参数值的先验知识。这种方法旨在根据实验数据调整参数，考虑实验不确定性信息，并为所有调整后的参数提供不确定性。该方法旨在通过修改这些模型的自由参数来提高模型的准确性，从而更好地再现实验数据，同时估计这些参数的不确定性及其对模型输出的影响。我们的目标是证明广义最小二乘法可以应用于蒙特卡洛模型的情况下。我们提出了一个概念验证，针对核物理中的INCL/ABLA模型组合的特定情况下的蒙特卡洛模型。我们讨论了将此方法应用于高能散裂模型时的挑战，特别是模型的大运行时间和模型的随机性。我们的结果显示，这个框架也可以应用于类似的情况，在这种情况下，应该推断/改进计算成本高昂的蒙特卡洛代码的参数。 显示英文摘要

摘要： The accuracy and precision of high-energy spallation models are key issues for the design and development of new applications and experiments. We present a method to estimate model parameters and associated uncertainties by leveraging the Bayesian version of the Generalised Least Squares method, which enables us to incorporate prior knowledge on the parameter values. This approach is designed to adjust parameters based on experimental data, accounting for experimental uncertainty information, and providing uncertainties for all adjusted parameters. This approach is designed in order both to improve the accuracy of models through the modification of free parameters of these models, which results in a better reproduction of experimental data, and to estimate the uncertainties of these parameters and, by extension, their impacts on the model output. We aim at demonstrating the Generalised Least Square method can be applied in the case of Monte Carlo models. We present a proof-of-concept for Monte Carlo models in the specific case of nuclear physics with the model combination INCL/ABLA. We discuss the challenges in the application of this method to high-energy spallation models, notably the large runtime and the stochasticity of the models. Our results indicate this framework can also be applied to analogous situations where parameters of a computationally expensive Monte Carlo code should be inferred/improved.