论文标题
对日志样品的机器学习在Parton分布的全球分析中
Machine learning of log-likelihood functions in global analysis of parton distributions
论文作者
论文摘要
对Parton分布功能（PDF）的现代分析需要计算数千个实验数据点的对数似然函数，并扫描具有数十种自由度的多维参数空间。在常规分析中，Hessian近似已被广泛用于估计PDF不确定性。由于计算限制，Lagrange乘数（LM）扫描是一种更忠实的方法，而不是使用更忠实的方法，并且是本研究的主要重点。我们建议使用神经网络（NNS）和机器学习技术来建模对数似然函数或横截面的曲线，以便为了克服这些限制，这些局限性超出了二次近似值，并确保了整个参数空间的有效扫描。我们通过为各种目标函数构造NNS并对HADRON COLLIDERS上的PDF和横截面进行LM扫描来证明新方法在CT18全局PDF框架中的效率。我们进一步研究了Nomad Dimuon数据对使用新方法约束PDF的影响，并发现增强的奇怪夸克分布并减少了PDF的不确定性。此外，我们展示了如何通过在SM有效场理论中使用PDF和Wilson系数的联合拟合来限制标准模型（BSM）以外的新物理。
Modern analysis on parton distribution functions (PDFs) requires calculations of the log-likelihood functions from thousands of experimental data points, and scans of multi-dimensional parameter space with tens of degrees of freedom. In conventional analysis the Hessian approximation has been widely used for the estimation of the PDF uncertainties.The Lagrange Multiplier (LM) scan while being a more faithful method is less used due to computational limitations, and is the main focus of this study. We propose to use Neural Networks (NNs) and machine learning techniques to model the profile of the log-likelihood functions or cross sections for multi-dimensional parameter space in order to overcome those limitations which work beyond the quadratic approximations and meanwhile ensures efficient scans of the full parameter space. We demonstrate the efficiency of the new approach in the framework of the CT18 global analysis of PDFs by constructing NNs for various target functions, and performing LM scans on PDFs and cross sections at hadron colliders. We further study the impact of the NOMAD dimuon data on constraining PDFs with the new approach, and find enhanced strange-quark distributions and reduced PDF uncertainties. Moreover, we show how the approach can be used to constrain new physics beyond the Standard Model (BSM) by a joint fit of both PDFs and Wilson coefficients of operators in the SM effective field theory.
