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arXiv 2005.11006hep-th
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正几何下$ϕ^p$树振幅的权和递归关系

Weights and recursion relations for $ϕ^p$ tree amplitudes from the positive geometry

Ryota Kojima

摘要:

最近,在$ϕ^p$理论\cite{Raman:2019utu}中,提出了运动空间中的手风琴面体作为平面树级散射振幅的正几何。散射振幅被表示为具有适当权值的某些一致面体的标准形上的加权和。通过要求加权和对应于散射振幅来确定这些权重。这意味着我们需要从量子场论中获得额外的数据,才能从几何学中计算振幅。即使在这种$ϕ^p$理论中,散射振幅是否完全由几何结构得到,也是一个重要的问题。在本文中,我们表明,这些权重是完全确定的手风琴面体的因式分解性质。这意味着,手风琴的几何形状足以确定这些权重。除此之外,我们还研究了$ϕ^p$振幅的单参数递归关系。通过对ABHY协面体\cite{Arkani-Hamed:2017tmz}的三角剖分,得到了$ϕ^3$振幅的单参数“BCFW”式递推关系.在此基础上,利用\cite{Arkani-Hamed:2019vag, Yang:2019esm}中广义ABHY-结合面体的投影三角剖分,提出了一种新的递推关系。我们将这些单参数递归关系推广到$ϕ^p$振幅,并解释为三角形的accordiohedra。

英文摘要:

Recently, the accordiohedron in kinematic space was proposed as the positive geometry for planar tree-level scattering amplitudes in the $ϕ^p$ theory \cite{Raman:2019utu}. The scattering amplitudes are given as a weighted sum over canonical forms of some accordiohedra with appropriate weights. These weights were determined by demanding that the weighted sum corresponds to the scattering amplitudes. It means that we need additional data from the quantum field theory to compute amplitudes from the geometry. It has been an important problem whether scattering amplitudes are completely obtained from only the geometry even in this $ϕ^p$ theory. In this paper, we show that these weights are completely determined by the factorization property of the accordiohedron. It means that the geometry of the accordiohedron is enough to determine these weights. In addition to this, we study one-parameter recursion relations for the $ϕ^p$ amplitudes. The one-parameter "BCFW"-like recursion relation for the $ϕ^3$ amplitudes was obtained from the triangulation of the ABHY-associahedron \cite{Arkani-Hamed:2017tmz}. After this, a new recursion relation was proposed from the projecting triangulation of the generalized ABHY-associahedron in \cite{Arkani-Hamed:2019vag, Yang:2019esm}. We generalize these one-parameter recursion relations to the $ϕ^p$ amplitudes and interpret as triangulations of the accordiohedra.

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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