百分之一精度的质子结构之路
The Path to Proton Structure at One-Percent Accuracy
摘要：
我们提出了一套新的部分子分布函数（PDF）的基础上，一个完整的全球数据集和机器学习技术：NNPDF 4.0。我们用44个新数据集扩展了NNPDF3.1的测定，其中大部分来自LHC。我们通过超参数优化得到一种新的方法，从而建立在随机梯度下降的有效拟合算法。我们使用NNLO QCD计算和帐户的NLO电弱修正和核的不确定性。PDF描述中的理论改进包括系统地实现正性约束和求和规则的可积性。我们通过封闭测试和“未来测试”（即向后和向前数据兼容性测试）来验证我们的方法，并评估其稳定性，特别是在PDF参数化基础发生变化时。我们研究了我们的数据集的内部兼容性，并调查结果的依赖性都在输入数据集和拟合方法的选择。我们进行了第一次研究NNPDF4.0的代表性LHC过程的唯象影响。用于生成NNPDF4.0的软件框架作为开源软件包与文档和示例一起提供。
英文摘要：
We present a new set of parton distribution functions (PDFs) based on a fully global dataset and machine learning techniques: NNPDF4.0. We expand the NNPDF3.1 determination with 44 new datasets, mostly from the LHC. We derive a novel methodology through hyperparameter optimisation, leading to an efficient fitting algorithm built upon stochastic gradient descent. We use NNLO QCD calculations and account for NLO electroweak corrections and nuclear uncertainties. Theoretical improvements in the PDF description include a systematic implementation of positivity constraints and integrability of sum rules. We validate our methodology by means of closure tests and "future tests" (i.e. tests of backward and forward data compatibility), and assess its stability, specifically upon changes of PDF parametrization basis. We study the internal compatibility of our dataset, and investigate the dependence of results both upon the choice of input dataset and of fitting methodology. We perform a first study of the phenomenological implications of NNPDF4.0 on representative LHC processes. The software framework used to produce NNPDF4.0 is made available as an open-source package together with documentation and examples.
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