具有因子分解感知能力的单圈矩阵元仿真
One-loop matrix element emulation with factorisation awareness
AI总结：
提出一种基于矩阵元因子分解性质的单圈矩阵元仿真策略，在高多重数过程中仍达到百分比级精度，并可低成本将训练集扩大数个数量级。
摘要：
本文提出了一种针对单圈矩阵元的仿真策略。该策略基于矩阵元的因子分解性质，是对 arXiv:2107.06625 所呈现工作的扩展。我们表明，即使对于高多重数过程，也能实现百分比量级的精度。所获得的逐点精度远高于训练样本的统计精度，这使我们能够利用该模型将训练集规模扩大数个数量级，而无需额外计算昂贵的单圈矩阵元。
英文摘要：
In this article we present an emulation strategy for one-loop matrix elements. This strategy is based on the factorisation properties of matrix elements and is an extension of the work presented in arXiv:2107.06625. We show that a percent-level accuracy can be achieved even for large multiplicity processes. The point accuracy obtained is such that it dwarfs the statistical accuracy of the training sample which allows us to use our model to augment the size of the training set by orders of magnitude without additional evaluations of expensive one-loop matrix elements.
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