发表机构
University of Jyväskylä（于韦斯屈莱大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出将质子部分子分布函数推断问题重构为线性张量重建反问题，利用微扰QCD积分方程结构，实现无模型偏差的PDF提取。
AI 中文摘要
我们证明，从深度非弹性散射数据约束质子部分子分布函数的推断问题可以表述为一个线性张量重建反问题。这意味着，不是将模型参数拟合到世界数据，而是可以构建一种重建方法来求解一组耦合的线性泛函积分方程。我们利用由微扰QCD和全局分析定义的积分方程的数学结构，将全局分析问题表述为耦合的线性积分方程组，并构建一种原理验证方法来解决它。为了具体地表述这种方法，我们回顾了包含虚光子、中性流、带电流、重味产生和中微子深度非弹性轻子-质子散射的所有次领头阶精度结果。这种数学推断方法为从深度非弹性散射世界数据中无模型偏差地提取质子PDF开辟了一条道路，符合数学反问题中采用的间接测量精神，包括在紧密遵循微扰QCD和全局分析的既定范式的同时，以减少模型参数化偏差的方式进行稳健的不确定性估计。
英文摘要
We show that the inference problem of constraining the parton distribution functions of the proton from deeply inelastic scattering data can be formulated as a linear tensor reconstruction inverse problem. This means that instead of fitting model parameters to the world data, a reconstructive approach to solve a system of coupled linear functional integral equations can be formulated. We leverage the mathematical structures of the integral equations defined by perturbative QCD and global analysis to pose the problem of global analysis as a coupled system of linear integral equations, and construct a proof-of-principle methodology to solve it. To formulate this approach concretely, we review all next-to-leading order accuracy results for inclusive virtual photon, neutral current, charged current, heavy flavor production, and neutrino deep-inelastic lepton--proton scattering. This mathematical methodology of inference opens a path towards a model-bias-free extraction of the proton PDFs from the world data of deeply inelastic scattering in the spirit of indirect measurement employed in mathematical inverse problems, including robust estimation of uncertainties with reduced bias from model parametrization, while closely adhering to the established paradigm of perturbative QCD and global analysis.
Comments121 pages, 1 figure
