SYMBA: Symbolic Computation of Squared Amplitudes in High Energy Physics with Machine Learning
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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 transformer model 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 89.0 processes, respectively. We discuss the performance of the current model, its limitations and possible future directions for this work.
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