Speaker
Description
The calculation of particle interaction squared amplitudes is a key step in the calculation of cross sections in high-energy physics. These lengthy calculations are currently done using domain-specific symbolic algebra tools, where the time required for the calculations grows rapidly with the number of final state particles involved. 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 beginning. We developed a transformer-based sequence-to-sequence model inspired by natural language processing that is able to accurately predict squared amplitudes of QCD and QED processes, respectively, when trained on symbolic sequence pairs. The goal of this work is to significantly reduce the computational time and, more importantly, build a model that scales well with the number of final state particles. To the best of our knowledge, this model (SYMBA) is the first model 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.
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