SYMBA: Symbolic Computation of Amplitudes in High-Energy Physics with Machine Learning

While machine learning is widely used for numerical calculations in high-energy physics, analytical calculations using machine learning are still in their infancy. In this talk, we show that a transformer model is able to map correctly interaction amplitudes to their square, averaged over initial and summed over final particle degrees of freedom, for 97.6% and 99% of QCD and QED processes, respectively, at a speed that is up to two 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.