AI 中文总结
本研究通过PARTONS框架，采用机器学习的两种建模方法提取核子上的DVCS振幅，检验GPDs普适性并对比格点QCD的减法常数预测，推动理论与现象学的良性循环。
AI 中文摘要
本工作通过对实验数据的全局分析，首次对质子和中子上的深度虚康普顿散射（DVCS）振幅进行了现代提取。这些振幅是连接广义部分子分布（GPDs）与实验可观测量的关键中间量。该分析依赖于最近集成到PARTONS软件生态系统中的新型提取框架。我们对DVCS振幅采用两种不同的建模方法，均运用了机器学习技术：一种是与模型无关的方法，另一种是理论增强的方法。我们首次纳入了螺旋度翻转振幅的提取，该振幅对高twist效应尤为敏感。此外，我们利用DVCS与类时康普顿散射（TCS）之间的既定关系，将我们提取的振幅与TCS数据进行了比较，以此作为对GPDs普适性的重要检验。我们还提取了DVCS减法常数，而格点量子色动力学（lattice QCD）已获得了该常数的从头算预测。这使我们首次能够将这些预测与DVCS数据进行对比，开启了第一性原理理论与现象学之间的良性循环。
英文摘要
This work presents a modern extraction of deeply virtual Compton scattering (DVCS) amplitudes off the proton and off the neutron through a global analysis of experimental data. These amplitudes serve as a crucial intermediate quantity linking generalized parton distributions (GPDs) to experimental observables. The analysis relies on a novel extraction framework recently integrated into the PARTONS software ecosystem. We employ two distinct modelling approaches for the DVCS amplitudes, both utilizing machine learning techniques: one model-agnostic and the other theory-augmented. For the first time, we incorporate the extraction of helicity-flip amplitudes, which are particularly sensitive to higher-twist effects. Furthermore, we compare our extracted amplitudes to timelike Compton scattering (TCS) data using the established relations between DVCS and TCS, serving as an important test of universality of GPDs. We also extract the DVCS subtraction constant, for which ab initio predictions from lattice QCD have been obtained. This allows us to confront these predictions with DVCS data for the first time, initiating a new virtuous cycle between first-principles theory and phenomenology.
Comments49 pages, 21 figures, 2 tables
