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arXiv 2607.23485hep-ph

在HERA、RHIC和LHC上对深度非弹性散射和前向强子产生进行同时的色玻璃凝聚拟合

Simultaneous Color Glass Condensate fit to deep inelastic scattering and forward hadron production at HERA, RHIC, and the LHC

Piotr Korcyl, Truong My Hau Le, Farid Salazar, Tomasz Stebel

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中文总结 AI 辅助

该研究首次对HERA的深度非弹性散射、RHIC和LHC的前向单举强子产生进行同时拟合,利用BK演化获取偶极振幅,通过特定方程和K因子实现全局χ²/自由度接近1,发现两可观测量互补,量化了K因子相关不确定性并分析了误差。

中文摘要 AI 辅助

我们首次对来自HERA的深度非弹性散射(DIS)约化截面以及来自RHIC和LHC的前向单举强子产生(SIHP)进行了同时拟合,其中偶极振幅是从色玻璃凝聚有效理论内的Balitsky--Kovchegov(BK)演化中获得的。我们证明,使用具有跑动耦合的领头阶BK方程(有或没有运动学约束)以及一个考虑高阶修正的每个对撞机的常数K因子,在两种方案中都能实现全局χ²/自由度接近1。所需的K因子在RHIC比在LHC大约大两倍,这与前向产生的阈值重整化单圈研究显著一致。在我们的设置和现有数据下,我们发现这两个可观测量是互补的:SIHP数据收紧了对偶极振幅演化速度的约束,而不会与DIS描述产生矛盾。我们进一步量化了K因子对碎裂函数集和因子化尺度的依赖性,确定这些是未来前向强子产生精确分析中要解决的主要系统不确定性。我们使用Hessian方法进行了全面的不确定性分析,并通过蒙特卡罗贝叶斯推断研究进行了验证。

英文摘要

We present the first simultaneous fit to deep inelastic scattering (DIS) reduced cross sections from HERA and forward single inclusive hadron production (SIHP) from RHIC and the LHC in which the dipole amplitude is obtained from Balitsky--Kovchegov (BK) evolution within the Color Glass Condensate effective theory. We demonstrate that, using the LO BK equation with running coupling (with or without kinematical constraint) and a constant per-collider $K$-factor accounting for higher-order corrections, a global $χ^2/\mathrm{d.o.f.}$ close to unity is achieved in both schemes. The required $K$-factors are about a factor of two larger at RHIC than at the LHC, remarkably in agreement with threshold-resummed one-loop studies of forward production. In our setup and with the data available, we find the two observables to be complementary: the SIHP data tighten the constraint on the evolution speed of the dipole amplitude without introducing tension with the DIS description. We further quantify the dependence of the $K$-factors on the fragmentation-function set and the factorization scale, identifying these as the dominant systematic uncertainties to be addressed in future precision analyses of forward hadron production. We perform a full uncertainty analysis using the Hessian method, validated against a Monte Carlo Bayesian inference study.

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