论文标题
nnnpdf2.0：核与LHC数据中的夸克风味分离
nNNPDF2.0: Quark Flavor Separation in Nuclei from LHC Data
论文作者
论文摘要
我们使用基于NNPDF框架的机器学习方法和蒙特卡洛技术对核部分分布函数（NPDF）呈现模型无关的确定。我们先前分析中使用的中性深度弹性核结构功能NNNPDF1.0，通过带电 - 电流散射的包含和符号标记的横截面补充。此外，我们在$ \ sqrt {s} = 5.02 $ tev和8.16 tev的质子铅碰撞中包括W和Z leptonic快速分布的所有可用测量值。由此产生的NPDF确定NNNPDF2.0可以很好地描述所有数据集。除了量化影响单个夸克和古怪的核修饰外，我们还研究了对陌生性的含义，评估动量和价和价值规则在NPDF提取中的作用，以及目前对代表性现象学应用的预测。我们的结果通过LHAPDF库提供，强调了高能撞机测量器以鲁棒方式探测核动力学的潜力。
We present a model-independent determination of the nuclear parton distribution functions (nPDFs) using machine learning methods and Monte Carlo techniques based on the NNPDF framework. The neutral-current deep-inelastic nuclear structure functions used in our previous analysis, nNNPDF1.0, are complemented by inclusive and charm-tagged cross-sections from charged-current scattering. Furthermore, we include all available measurements of W and Z leptonic rapidity distributions in proton-lead collisions from ATLAS and CMS at $\sqrt{s}=5.02$ TeV and 8.16 TeV. The resulting nPDF determination, nNNPDF2.0, achieves a good description of all datasets. In addition to quantifying the nuclear modifications affecting individual quarks and antiquarks, we examine the implications for strangeness, assess the role that the momentum and valence sum rules play in nPDF extractions, and present predictions for representative phenomenological applications. Our results, made available via the LHAPDF library, highlight the potential of high-energy collider measurements to probe nuclear dynamics in a robust manner.
