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电子-质子测量在电子-离子对撞机早期科学阶段的前景

Inclusive electron-proton measurement prospects in the Electron-Ion Collider early science stage

Universidad Complutense de Madrid(马德里康普顿斯大学);Deutsches Elektronen-Synchrotron DESY(德国电子同步辐射中心);University of Birmingham(伯明翰大学)

Javier Jiménez-López, Stephen Maple, Paul R. Newman, Katarzyna Wichmann

AI总结:

研究利用EIC早期数据提取质子结构函数、PDFs及强耦合常数,展示多束能配置提升测量精度,为QCD研究提供重要基础。

摘要:

我们探讨了利用未来电子-离子对撞机(EIC)的早期科学数据提取质子结构函数、质子部分子密度函数(PDFs)以及强耦合常数α_s(M_z^2)的潜力,单独使用以及结合HERA数据。考虑了在两种或三种EIC束能配置下收集低亮度样本的不同场景。使用罗斯文分离法以模型无关的方式从模拟数据中提取质子结构函数F2和FL,显示使用三种总能量时FL的提取精度显著高于两种,同时获得比以往更高的F2精度。此外,加入第三束配置对提取可通过EIC早期数据实现的强耦合常数α_s(M_z^2)也有益。此外,当加入这些数据时,质子PDFs的精度得到提高,特别是对于较大的Bjorken-x值,适用于两种和三种EIC束能配置。这些研究显示,EIC数据在数据收集的前五年内将已然是强相互作用量子色动力学(QCD)的强有力探测工具。

英文摘要:

We explore the potential for extracting proton structure functions, proton parton density functions (PDFs), and the strong coupling $α_s(M_z^2)$, using early science data from the future Electron-Ion Collider (EIC), both standalone, and in combination with HERA data. Different scenarios are considered in which samples with modest luminosity are collected at either two or three EIC beam energy configurations. The Rosenbluth separation method is used to extract the proton structure functions $F_2$ and $F_L$ from simulated data in a model-independent manner, showing that $F_L$ can be extracted significantly more precisely with three centre of mass energies than with two, whilst also obtaining $F_2$ to higher precision than has been achieved previously. The inclusion of a third beam configuration is also beneficial in the extraction of the strong coupling $α_s(M_z^2)$ that is obtainable with unprecedented experimental precision with the early EIC data. Additionally, the precision of the proton PDFs is improved when adding these data, especially for large values of Bjorken-$x$, for both two and three EIC beam energy configurations. These studies show that EIC data will already be a highly competitive probe of perturbative Quantum Chromodynamics within the first five years of data taking.

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。

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