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arXiv 2201.06586hep-phhep-ex
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部分子分布全局分析中对数似然函数的机器学习

Machine learning of log-likelihood functions in global analysis of parton distributions

DianYu Liu, ChuanLe Sun, Jun Gao

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49 pages, 26 figures
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摘要：

部分子分布函数（PDF）的现代分析需要从数千个实验数据点计算对数似然函数，并扫描具有数十个自由度的多维参数空间。在传统的分析中，Hessian近似被广泛应用于PDF不确定度的估计，而Lagrange乘子（LM）扫描虽然是一种更可靠的方法，但由于计算量的限制，使用较少，是本文研究的主要重点。我们建议使用神经网络（NN）和机器学习技术来建模的轮廓的对数似然函数或截面的多维参数空间，以克服这些限制，超出了二次近似的工作，同时确保有效的扫描的整个参数空间。我们证明了新方法的效率的框架中的CT18全球分析的PDF构建NN为各种目标函数，并在强子对撞机上的PDF和截面进行LM扫描。我们进一步研究了NOMAD双μ子数据对约束PDF的影响，发现奇异夸克分布增强，PDF不确定性降低。此外，我们展示了如何可以使用的方法来约束新的物理超越标准模型（BSM）的共同适合的PDF和威尔逊系数的运营商在SM有效场论。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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