SYMBA: Symbolic Computation of Squared Amplitudes in High Energy Physics with Machine Learning
This addresses the bottleneck of analytical calculations in high-energy physics for researchers, though it is incremental as it applies an existing machine learning method to a specific domain task.
The paper tackled the problem of computing squared amplitudes for cross sections in high-energy physics, which is time-consuming, by using a transformer model, achieving 97.6% and 99% accuracy for QCD and QED processes with orders of magnitude speedup.
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.