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arXiv 2210.02529hep-ph
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$x\ ightarrow 1$ 极限下深度非弹性散射中次领头阶功率的因子化

Factorization at subleading power in deep inelastic scattering in the $x\rightarrow 1$ limit

Michael Luke, Jyotirmoy Roy, Aris Spourdalakis

AI总结:

本文用无显式软/共线模式的软共线有效理论研究 DIS 端点区 NLP 因子化,证明重叠减除可在一圈阶消除虚假端点发散,并验证领头与次领头算符反常维度的非平凡关系。

摘要:

我们考察单举深度非弹性散射在次领头阶功率(NLP)下的端点区域。采用不包含显式软或共线模式的软共线有效理论方法,我们讨论了截面在 NLP 下的因子化,并表明:为消除领头功率处自由度重复计数而引入的重叠减除程序,保证了速率中的虚假端点发散在一圈阶的 NLP 下相消。为使这种相消在所有重整化标度下都发生,领头与次领头算符的反常维度之间必须存在一个非平凡关系;我们证明该关系在一圈阶成立。

英文摘要:

We examine the endpoint region of inclusive deep inelastic scattering at next-to-leading power (NLP). Using a soft-collinear effective theory approach with no explicit soft or collinear modes, we discuss the factorization of the cross section at NLP and show that the overlap subtraction procedure introduced to eliminate double counting of degrees of freedom at leading power ensures that spurious endpoint divergences in the rate cancel at NLP at one loop. For this cancellation to occur at all renormalization scales a nontrivial relation between the anomalous dimensions of the leading and subleading operators is required, which is demonstrated to hold at one loop.

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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