物理引导的全局提取通用小x双极振幅
Physics-Informed Global Extraction of the Universal Small-$x$ Dipole Amplitude
AI总结：
本文利用物理引导神经网络提取通用小x双极振幅，统一描述所有拟合可观测量并缓解传统拟合中的张力。
摘要：
我们从基于物理引导神经网络（PINN）的全局分析中提取出通用的小x双极散射振幅N(r,x_B)，而无需事先假设MV类型的初始条件参数化。该网络为N(r,x_B)提供了一个平滑且可微的替代模型，其快速性依赖被线性改进的Balitsky-Kovchegov演算方程所约束，同时其函数形式也受到深度非弹性散射（DIS）数据对缩减总横截面和魅力横截面、排斥J/ψ光产生测量以及动量空间双极振幅的正性要求的约束。所得到的单一通用振幅在统一框架内一致描述了所有拟合的可观测量，缓解了传统小x拟合中总横截面和魅力通道之间长期存在的张力。在拟合的动量域内，最佳提取的PINN解在完整的横动量范围内产生了一个平滑且非负的动量空间双极振幅。我们的结果为色玻璃凝聚子现象学在广泛高能过程中的稳健和良好行为的输入提供了支持。
英文摘要：
We extract the universal small-$x$ dipole scattering amplitude $N(r,x_B)$ from a global analysis based on a physics-informed neural network (PINN), without imposing a priori MV-type parametrization of the initial condition. The network provides a smooth and differentiable surrogate for $N(r,x_B)$, whose rapidity dependence is constrained by the collinearly improved Balitsky--Kovchegov evolution equation, while its functional form is simultaneously constrained by Deep Inelastic Scattering (DIS) data for the reduced total and charm cross sections, exclusive $J/ψ$ photoproduction measurements, and a positivity requirement for the momentum-space dipole amplitude. The resulting single universal amplitude consistently describes all fitted observables within a unified framework, alleviating the long-standing tension between total and charm channels encountered in conventional small-$x$ fits based on rigid parametric ansätze. Within the fitted kinematic domain, the best extracted PINN solution yields a smooth, non-negative momentum-space dipole over the full transverse-momentum range examined. Our results provide a robust and well-behaved input for Color Glass Condensate phenomenology across a broad class of high-energy processes.
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