arXiv 2601.201772026-01-28hep-ph

部分子碎片化函数的提取方法：基于物理信息的神经网络

Parton Fragmentation Functions Extracted with a Physics-Informed Neural Network

Central China Normal University（华中师范大学）；Fudan University（复旦大学）

Si-Wei Dai, Fu-Peng Li, Long-Gang Pang, Xin-Nian Wang, Ben-Wei Zhang, Han-Zhong Zhang

备注：: 22 pages, 15 figures

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

基于物理信息神经网络提取部分子碎片化函数，提升非微扰量的提取效率与通用性。

摘要：

许多高能强相互作用过程的可靠预测严重依赖于从现有实验数据中提取的非微扰部分子碎片化函数（FFs）。传统方法通常需要对FFs进行参数化形式，并根据Dokshitzer-Gribov-Lipatov-Altarelli-Parisi（DGLAP）演进方程进行额外的尺度演进。我们介绍了一种使用物理信息神经网络（PINN）确定部分子FFs的新方法。与传统方法不同，我们的方法不需要先验的参数化形式，并且直接将DGLAP演进方程整合到神经网络架构中，使FFs能够自动满足这些方程。我们利用这种新方法从介子谱中提取了新的部分子FFs，这些FFs是在电子-正电子湮灭过程中，使用次领界（NLO）在pQCD中的结果。为了验证我们的方法，我们使用提取的FFs计算了质子-（反）质子碰撞中的带电介子谱，并证明了在广泛的碰撞能量范围内（√s = 130, 200, 500, 630, 900, 1800, 2760, 5020, 5440, 7000 GeV）结果与实验数据吻合良好。我们的发现表明，PINN方法不仅简化了提取过程，还增强了FFs在不同能量尺度上的通用适用性。通过消除对参数化形式和额外DGLAP演进的需要，我们的方法代表了朝着快速准确提取非微扰量（如部分子碎片化函数和部分子分布函数）的重要一步。

英文摘要：

Reliable predictions of many high-energy strong interaction processes rely heavily on the non-perturbative parton fragmentation functions (FFs) extracted from existing experimental data. Conventional methods often require parameterized forms of FFs and additional scale evolution according to the Dokshitzer-Gribov-Lipatov-Altarelli-Parisi (DGLAP) evolution equations. We introduce a novel approach to determining parton FFs using a Physics-Informed Neural Network (PINN). Unlike traditional methods, our approach does not require prior parameterized forms and directly integrates the DGLAP evolution equations into the neural network architecture, allowing the FFs to automatically satisfy these equations. We present new sets of parton FFs extracted from hadron spectra in electron-positron annihilation processes at next-to-leading order (NLO) in pQCD using this new technique. To validate our approach, we calculate charged hadron spectra in proton-(anti)proton collisions using the extracted FFs and demonstrate that the results align well with experimental data across a large range of colliding energies ($\sqrt{s}$ = 130, 200, 500, 630, 900, 1800, 2760, 5020, 5440, 7000 GeV). Our findings indicate that the PINN method not only simplifies the extraction process but also enhances the universal applicability of FFs across different energy scales. By eliminating the need for parameterized forms and additional DGLAP evolution, our approach represents a significant step forward toward fast and accurate extractions of non-perturbative quantities such as parton fragmentations functions and parton distribution functions.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

AI总结：

摘要：

英文摘要：

相关论文