论文标题
在SIDIS数据的存在下，带电的强子片段化功能的神经网络QCD分析
Neural Network QCD analysis of charged hadron Fragmentation Functions in the presence of SIDIS data
论文作者
论文摘要
在本文中，我们提出了QCD分析，以提取未识别的光电强体的碎片函数（FFS），该函数有资源为SHK22.H，该函数从高能Lepton-Lepton-Lepton-Lepton an灭和Lepton-Hadron散射数据集。该分析包括来自所有可用的单一包含电子脉络体歼灭（SIA）工艺的数据，以及针对未识别的带电的强子生产的半包含的深度无弹性散射（SIDIS）测量。通过指南针实验测量的SIDIS数据可以使FFS的风味受到良好的约束。我们利用神经网络（NN）的分析衍生物在扰动QCD（PQCD）中的近代级（NLO）精度上拟合FF。对于所有实验不确定性的来源和Parton分布函数（PDFS）也暗示了Monte Carlo方法。 SHK22.H FFS集与文献中可用的最新QCD合适之间达到了非常好的协议，即JAM20和NNFF1.1H。此外，我们讨论了由SIDIS数据纳入提取的轻荷载体FFS所产生的影响。在NLO的全球QCD负载强子FFS导致的全球QCD为当前和未来的高能量测量的应用程序提供了宝贵的见解。
In this paper, we present a QCD analysis to extract the Fragmentation Functions (FFs) of unidentified light charged hadron entitled as SHK22.h from high-energy lepton-lepton annihilation and lepton-hadron scattering data sets. This analysis includes the data from all available single inclusive electron-positron annihilation (SIA) processes and semi-inclusive deep-inelastic scattering (SIDIS) measurements for the unidentified light charged hadron productions. The SIDIS data which has been measured by the COMPASS experiment could allow the flavor dependence of the FFs to be well constrained. We exploit the analytic derivative of the Neural Network (NN) for fitting of FFs at next-to-leading-order (NLO) accuracy in the perturbative QCD (pQCD). The Monte Carlo method is implied for all sources of experimental uncertainties and the Parton distribution functions (PDFs) as well. Very good agreements are achieved between the SHK22.h FFs set and the most recent QCD fits available in literature, namely JAM20 and NNFF1.1h. In addition, we discuss the impact arising from the inclusion of SIDIS data on the extracted light-charged hadron FFs. The global QCD resulting at NLO for charged hadron FFs provides valuable insights for applications in present and future high-energy measurement of charged hadron final state processes.
