标题： nNNPDF2.0：基于LHC数据的核子中夸克味分离 显示英文标题

标题： nNNPDF2.0: Quark Flavor Separation in Nuclei from LHC Data

摘要： 我们利用基于NNPDF框架的机器学习方法和蒙特卡罗技术，给出了无模型依赖的核部分子分布函数(nPDFs)的确定。 我们在之前的工作nNNPDF1.0中使用的中性流深击变分核结构函数，现在补充了来自带电流散射的包容性和魅力标记截面。 此外，我们包含了ATLAS和CMS在$\sqrt{s}=5.02$TeV 和 8.16 TeV 质子-铅碰撞中所有可用的W和Z轻子快速度分布测量数据。 由此得到的nPDF确定，即nNNPDF2.0，能够很好地描述所有数据集。 除了量化影响单个夸克和反夸克的核修正外，我们还研究了奇异夸克的影响，评估了动量和价求和规则在nPDF提取中的作用，并提出了代表性的现象学应用预测。 我们的结果通过LHAPDF库发布，突显了高能对撞机测量在稳健地探测核动力学方面的潜力。 显示英文摘要

摘要： 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.