论文标题
SYMBA：通过机器学习的高能物理中平方振幅的象征计算
SYMBA: Symbolic Computation of Squared Amplitudes in High Energy Physics with Machine Learning
论文作者
论文摘要
横截面是高能物理学中最重要的物理量之一，也是计算最耗时的。尽管事实证明，机器学习在高能物理学的数值计算中取得了非常成功的成功，但使用机器学习的分析计算仍处于起步阶段。在这项工作中，我们使用序列到序列模型，特别是变压器来计算横截面计算的关键元素，即相互作用的平方振幅。我们表明，变压器模型能够以比当前符号计算框架快的速度正确预测QCD和QED过程的平方幅度的97.6％和99％。我们讨论当前模型的性能，其局限性以及这项工作的未来指示。
The cross section is one of the most important physical quantities in high-energy physics and the most time consuming to compute. While machine learning has proven to be highly successful in numerical calculations in high-energy physics, analytical calculations using machine learning are still in their infancy. In this work, we use a sequence-to-sequence model, specifically, a transformer, to compute a key element of the cross section calculation, namely, the squared amplitude of an interaction. We show that a transformer model is able to predict correctly 97.6% and 99% of squared amplitudes of QCD and QED processes, respectively, at a speed that is up to orders of magnitude faster than current symbolic computation frameworks. We discuss the performance of the current model, its limitations and possible future directions for this work.
