论文标题
关于帕顿密度不确定性的确定
On the determination of uncertainties in parton densities
论文作者
论文摘要
我们回顾了用于估计量子相关函数中不确定性的各种方法，例如Parton分布函数（PDFS）。使用PDF的玩具模型，我们比较了传统的Hessian和数据重采样方法所产生的不确定性估计值，以及使用嵌套采样或混合Markov Chain Monte Carlo技术的明确贝叶斯分析。我们研究了来自神经网络方法的不确定性频段如何取决于网络培训的细节，以及它们与具有特定基础参数化的更传统方法获得的不确定性相比。我们的结果表明，在简化的PDF数据示例上使用神经网络有可能使不确定性充气，部分原因是交叉验证程序通常用于避免过度拟合数据。
We review various methods used to estimate uncertainties in quantum correlation functions, such as parton distribution functions (PDFs). Using a toy model of a PDF, we compare the uncertainty estimates yielded by the traditional Hessian and data resampling methods, as well as from explicitly Bayesian analyses using nested sampling or hybrid Markov chain Monte Carlo techniques. We investigate how uncertainty bands derived from neural network approaches depend on details of the network training, and how they compare to the uncertainties obtained from more traditional methods with a specific underlying parametrization. Our results show that utilizing a neural network on a simplified example of PDF data has the potential to inflate uncertainties, in part due to the cross validation procedure that is generally used to avoid overfitting data.
