We examine the quark-induced Drell-Yan process at next-to-leading power (NLP) in soft-collinear effective theory. Using an approach with no explicit soft or collinear modes, we discuss the factorization of the differential cross section in the small-$q_T$ hierarchy with $q^2\gg q_T^2\gg {\mathrm{\Lambda }}_{\mathrm{QCD}}^2$. We show that the cross section may be written in terms of matrix elements of power-suppressed operators $T_{(i,j)}$, which contribute to $O(q_T^2/q^2)$ coefficients of the usual parton distribution functions. We derive a factorization for this observable at NLP which allows the large logarithms in each of the relevant factors to be resummed. We discuss the cancellation of rapidity divergences and the overlap subtractions required to eliminate double counting at next-to-leading power.

中文翻译：

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EFT 中 Drell-Yan 过程中功率校正的因式分解

我们在软共线有效理论中以次超前功率 (NLP) 研究了夸克诱导的 Drell-Yan 过程。使用没有显式软或共线模式的方法，我们讨论了小$q_{吨}$ 层次结构 $q^2\gg q_{吨}^2\gg {\mathrm{\Lambda }}_{\mathrm{QCD}}^2$. 我们表明横截面可以用功率抑制算子的矩阵元素来写${吨}_{(\mathrm{一世},j)}$，这有助于 $哦(q_{吨}^2/q^2)$通常部分子分布函数的系数。我们在 NLP 中为这个可观测值推导出一个因式分解，它允许重新计算每个相关因子中的大对数。我们讨论消除快速发散和消除次领先功率重复计数所需的重叠减法。