A unique feature of generalised parton distributions is their relation to the QCD energy–momentum tensor. In particular, they provide access to the mechanical properties of the proton i.e. the distributions of pressure and shear stress induced by its quark and gluon structure. In principle the pressure distribution can be experimentally determined in a model-independent way from a dispersive analysis of deeply virtual Compton scattering data through the measurement of the subtraction constant. In practice the kinematic coverage and accuracy of existing experimental data make this endeavour a challenge. Elaborating on recent global fits of deeply virtual Compton scattering measurements using artificial neural networks, our analysis presents the current knowledge on this subtraction constant and assesses the impact of the most frequent systematic assumptions made in this field of research. This study will pave the way for future works when more precise data will become available, e.g. obtained in the foreseen electron-ion colliders EIC and EIcC.

广义帕顿分布的一个独特特征是它们与QCD能量动量张量的关系。特别地，它们提供了质子的机械性能的访问途径，即质子的夸克和胶子结构引起的压力和剪切应力的分布。原则上，可以通过模型的独立方式从深虚拟康普顿散射数据的色散分析到减法常数的测量来确定压力分布。在实践中，现有实验数据的运动学覆盖范围和准确性使这项工作面临挑战。详细阐述了使用人工神经网络进行的深层虚拟康普顿散射测量的最新全球拟合，我们的分析提供了有关该减法常数的最新知识，并评估了该研究领域中最常见的系统假设的影响。当可获得更精确的数据（例如在预见的电子离子对撞机EIC和EIcC中获得）时，这项研究将为将来的工作铺平道路。