Symbolic Computation of Squared Amplitudes in High Energy Physics with Machine Learning

Abstract

The calculation of particle interaction squared amplitudes is a key step in the calculation of cross sections in high-energy physics. These complex calculations are currently performed using domain-specific symbolic algebra tools, where the computational time escalates rapidly with an increase in the number of loops and final state particles. This dissertation introduces an innovative approach: employing a transformer-based sequence-to-sequence model capable of accurately predicting squared amplitudes of Standard Model processes up to one-loop order when trained on symbolic sequence pairs. The primary objective of this work is to significantly reduce the computational time and, more importantly, develop a model that efficiently scales with the complexity of the processes. To the best of our knowledge, this model is the first that encapsulates a wide range of symbolic squared amplitude calculations and, therefore, represents a potentially significant advance in using symbolic machine learning techniques for practical scientific computations.